Grado Department of Industrial and Systems Engineering

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Acoustofluidic particle trapping, manipulation, and release using dynamic-mode cantilever sensors
Johnson, Blake N.; Mutharasan, Raj (Royal Society of Chemistry, 2016-11-23)
We show here that dynamic-mode cantilever sensors enable acoustofluidic fluid mixing and trapping of suspended particles as well as the rapid manipulation and release of trapped micro-particles via mode switching in liquid. Resonant modes of piezoelectric cantilever sensors over the 0 to 8 MHz frequency range are investigated. Sensor impedance response, flow visualization studies using dye and micro-particle tracers (100 mum diameter), and finite element simulations of cantilever modal mechanics and acoustic streaming show fluid mixing and particle trapping configurations depend on the resonant mode shape. We found trapped particles could be: (1) rapidly manipulated on millimeter length scales, and (2) released from the cantilever surface after trapping by switching between low- and high-order resonant modes (less than 250 kHz and greater than 1 MHz, respectively). Such results suggest a potentially promising future for dynamic-mode cantilevers in separations, pumping and mixing applications as well as acoustofluidic-enhanced sensing applications.
Age-related strength loss affects non-stepping balance recovery
Koushyar, Hoda; Bieryla, Kathleen A.; Nussbaum, Maury A.; Madigan, Michael L. (Public Library of Science, 2019-01-18)
Aging is associated with a higher risk of falls, and an impaired ability to recover balance after a postural perturbation is an important contributing factor. In turn, this impaired recovery ability likely stems from age-related decrements in lower limb strength. The purpose of this study was to investigate the effects of age-related strength loss on non-stepping balance recovery capability after a perturbation while standing, without constraining movements to the ankle as in prior reports. Two experiments were conducted. In the first, five young adults (ages 20–30) and six community-dwelling older adults (ages 70–80) recovered their balance, without stepping, from a backward displacement of a support surface. Balance recovery capability was quantified as the maximal backward platform displacement that a subject could withstand without stepping. The maximal platform displacement was 27% smaller among the older group (11.8±2.1 cm) vs. the young group (16.2±2.6 cm). In the second experiment, forward dynamic simulations of a two-segment, rigid-body model were used to investigate the effects of manipulating strength in the hip extensors/flexors and ankle plantar flexors/dorsiflexors. In these, typical age-related reductions in strength were included. The model predicted lower maximal platform displacements with age-related reductions only in plantar flexion and hip flexion strength. These findings support the previously reported age-related loss of balance recovery ability, and an important role for plantar flexor strength in this ability. © 2019 Koushyar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia
Alshammari, Sultanah Mohammed; Ba-Aoum, Mohammed Hassan; Alganmi, Nofe Ateq; Allinjawi, Arwa AbdulAziz (MDPI, 2021-08-12)
The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.
An Algorithm for Fast Generation of Bivariate Poisson Random Vectors
Shin, K.; Pasupathy, R. (INFORMS, 2010)
We present the "trivariate reduction extension" (TREx)-an exact algorithm for the fast generation of bivariate Poisson random vectors. Like the normal-to-anything (NORTA) procedure, TREx has two phases: a preprocessing phase when the required algorithm parameters are identified, and a generation phase when the parameters identified during the preprocessing phase are used to generate the desired Poisson vector. We prove that the proposed algorithm covers the entire range of theoretically feasible correlations, and we provide efficient-computation directives and rigorous bounds for truncation error control. We demonstrate through extensive numerical tests that TREx, being a specialized algorithm for Poisson vectors, has a preprocessing phase that is uniformly a hundred to a thousand times faster than a fast implementation of NORTA. The generation phases of TREx and NORTA are comparable in speed, with that of TREx being marginally faster. All code is publicly available.
Applying variance reduction ideas in queuing simulations
Ross, S. M.; Lin, K. Y. (Cambridge University Press, 2001)
Variance reduction techniques are often underused in simulation studies. In this article, we indicate how certain ones can be efficiently employed when analyzing queuing models. The first technique considered is that of dynamic stratified sampling; the second is the utilization of multiple control variates; the third concerns the replacement of random variables by their conditional expectations when trying to estimate the expected value of a sum of random variables.
Approximate Positively Correlated Distributions and Approximation Algorithms for D-optimal Design
Singh, Manoj; Xie, W. (2018-02-26)
Experimental design is a classical problem in statistics and has also found new applications in machine learning. In the experimental design problem, the aim is to estimate an unknown vector x in m-dimensions from linear measurements where a Gaussian noise is introduced in each measurement. The goal is to pick k out of the given n experiments so as to make the most accurate estimate of the unknown parameter x. Given a set S of chosen experiments, the most likelihood estimate x' can be obtained by a least squares computation. One of the robust measures of error estimation is the D-optimality criterion which aims to minimize the generalized variance of the estimator. This corresponds to minimizing the volume of the standard confidence ellipsoid for the estimation error x-x'. The problem gives rise to two natural variants depending on whether repetitions are allowed or not. The latter variant, while being more general, has also found applications in the geographical location of sensors. In this work, we first show that a 1/e-approximation for the D-optimal design problem with and without repetitions giving us the first constant factor approximation for the problem. We also consider the case when the number of experiments chosen is much larger than the dimension of the measurements and provide an asymptotically optimal approximation algorithm.
AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research
Gabbard, Joseph L.; Smith, Missie; Tanous, Kyle; Kim, Hyungil; Jonas, Bryan (Frontiers, 2019-10-23)
Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers to deliver higher quality HUDs with increased fields of view, little work has been done to understand how best to design and assess AR HUD user interfaces, and how to quantify their effects on driver behavior, performance, and ultimately safety. This paper reports on a novel, low-cost, immersive driving simulator created using a myriad of custom hardware and software technologies specifically to examine basic and applied research questions related to AR HUDs usage when driving. We describe our experiences developing simulator hardware and software and detail a user study that examines driver performance, visual attention, and preferences using two AR navigation interfaces. Results suggest that conformal AR graphics may not be inherently better than other HUD interfaces. We include lessons learned from our simulator development experiences, results of the user study and conclude with limitations and future work.
Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests
Yu, Lisha; Zhao, Yang; Wang, Hailiang; Sun, Tien-Lung; Murphy, Terrence E.; Tsui, Kwok-Leung (2021-03-25)
Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.
Beyond Finding Change: multitemporal Landsat for forest monitoring and management
Wynne, Randolph H.; Thomas, Valerie A.; Brooks, Evan B.; Coulston, J. O.; Derwin, Jill M.; Liknes, Greg C.; Yang, Z.; Fox, Thomas R.; Ghannam, S.; Abbott, A. Lynn; House, M. N.; Saxena, R.; Watson, Layne T.; Gopalakrishnan, Ranjith (2017-07)
Take homes

Tobler’s Law still in effect with time series – spatial autocorrelation in temporal coherence can help in both preprocessing and estimation

Continual process improvement in extant algorithms needed

Need additional means by which variations within (parameterization) and across algorithms addressed (the Reverend…)

Time series improving higher order products (example with NLCD TCC) enabling near continuous monitoring
C-NORTA: A Rejection Procedure for Sampling from the Tail of Bivariate NORTA Distributions
Ghosh, Samik; Pasupathy, R. (INFORMS, 2012)
We propose C-NORTA, an exact algorithm to generate random variates from the tail of a bivariate NORTA random vector. (A NORTA random vector is specified by a pair of marginals and a rank or product-moment correlation, and it is sampled using the popular NORmal-To-Anything procedure.) We first demonstrate that a rejection-based adaptation of NORTA on such constrained random vector generation problems may often be fundamentally intractable. We then develop the C-NORTA algorithm, relying on strategic conditioning of the NORTA vector, followed by efficient approximation and acceptance/rejection steps. We show that, in a certain precise asymptotic sense, the sampling efficiency of C-NORTA is exponentially larger than what is achievable through a naive application of NORTA. Furthermore, for at least a certain class of problems, we show that the acceptance probability within C-NORTA decays only linearly with respect to a defined rarity parameter. The corresponding decay rate achievable through a naive adaptation of NORTA is exponential. We provide directives for efficient implementation.
Can a Patient's In-Hospital Length of Stay and Mortality Be Explained by Early-Risk Assessments?
Azadeh-Fard, Nasibeh; Ghaffarzadegan, Navid; Camelio, Jaime A. (PLOS, 2016-09-15)
Objective To assess whether a patient’s in-hospital length of stay (LOS) and mortality can be explained by early objective and/or physicians’ subjective-risk assessments. Data Sources/Study Setting Analysis of a detailed dataset of 1,021 patients admitted to a large U.S. hospital between January and September 2014. Study Design We empirically test the explanatory power of objective and subjective early-risk assessments using various linear and logistic regression models. Principal Findings The objective measures of early warning can only weakly explain LOS and mortality. When controlled for various vital signs and demographics, objective signs lose their explanatory power. LOS and death are more associated with physicians’ early subjective risk assessments than the objective measures. Conclusions Explaining LOS and mortality require variables beyond patients’ initial medical risk measures. LOS and in-hospital mortality are more associated with the way in which the human element of healthcare service (e.g., physicians) perceives and reacts to the risks.
Capturing multi-stage fuzzy uncertainties in hybrid system dynamics and agent-based models for enhancing policy implementation in health systems research
Liu, Shiyong; Triantis, Konstantinos P.; Zhao, Li; Wang, Youfa (PLOS, 2018-04-25)
Background In practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model. Methodology In a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions. Results In this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM. Conclusions This research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.
The CCP Selector: Scalable Algorithms for Sparse Ridge Regression from Chance-Constrained Programming
Xie, Weijun; Deng, Xinwei (2018-06-11)
Sparse regression and variable selection for large-scale data have been rapidly developed in the past decades. This work focuses on sparse ridge regression, which considers the exact $L_0$ norm to pursue the sparsity. We pave out a theoretical foundation to understand why many existing approaches may not work well for this problem, in particular on large-scale datasets. Inspired by reformulating the problem as a chance-constrained program, we derive a novel mixed integer second order conic (MISOC) reformulation and prove that its continuous relaxation is equivalent to that of the convex integer formulation proposed in a recent work. Based upon these two formulations, we develop two new scalable algorithms, the greedy and randomized algorithms, for sparse ridge regression with desirable theoretical properties. The proposed algorithms are proved to yield near-optimal solutions under mild conditions. In the case of much larger dimensions, we propose to integrate the greedy algorithm with the randomized algorithm, which can greedily search the features from the nonzero subset identified by the continuous relaxation of the MISOC formulation. The merits of the proposed methods are elaborated through a set of numerical examples in comparison with several existing ones.
Classifying Diverse Physical Activities Using “Smart Garments”
Mokhlespour Esfahani, Mohammad Iman; Nussbaum, Maury A. (MDPI, 2019-07-16)
Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.
Classifying Step and Spin Turns Using Wireless Gyroscopes and Implications for Fall Risk Assessments
Fino, Peter C.; Frames, Christopher W.; Lockhart, Thurmon E. (MDPI, 2015-05-06)
Recent studies have reported a greater prevalence of spin turns, which are more unstable than step turns, in older adults compared to young adults in laboratory settings. Currently, turning strategies can only be identified through visual observation, either in-person or through video. This paper presents two unique methods and their combination to remotely monitor turning behavior using three uniaxial gyroscopes. Five young adults performed 90° turns at slow, normal, and fast walking speeds around a variety of obstacles while instrumented with three IMUs (attached on the trunk, left and right shank). Raw data from 360 trials were analyzed. Compared to visual classification, the two IMU methods’ sensitivity/specificity to detecting spin turns were 76.1%/76.7% and 76.1%/84.4%, respectively. When the two methods were combined, the IMU had an overall 86.8% sensitivity and 92.2% specificity, with 89.4%/100% sensitivity/specificity at slow speeds. This combined method can be implemented into wireless fall prevention systems and used to identify increased use of spin turns. This method allows for longitudinal monitoring of turning strategies and allows researchers to test for potential associations between the frequency of spin turns and clinically relevant outcomes (e.g., falls) in non-laboratory settings.
A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data
Tang, Liyang; Zhao, Yang; Tsui, Kwok-Leung; He, Yuxin; Pan, Liwei (MDPI, 2020-08-13)
Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework.
Collaborative task performance for learning using a virtual environment
Bystrom, Karl-Erik; Barfield, Woodrow S. (MIT Press, 1999-08-01)
This paper describes a study on the sense of presence and task performance in a virtual environment as affected by copresence (one subject working alone versus two subjects working as partners), level of control (control of movement and control of navigation through the virtual environment), and head tracking. Twenty subjects navigated through six versions of a virtual environment and were asked to identify changes in locations of objects within the environment. After each trial, subjects completed a questionnaire designed to assess their level of presence within the virtual environment. Results indicated that collaboration did not increase the sense of presence in the virtual environment, but did improve the quality of the experience in the virtual environment. Level of control did not affect the sense of presence, but subjects did prefer to control both movement and navigation. Head tracking did not affect the sense of presence, but did contribute to the spatial realism of the virtual environment. Task performance was affected by the presence of another individual, by head tracking, and by level of control, with subjects performing significantly more poorly when they were both alone and without control and head tracking. In addition, a factor analysis indicated that questions designed to assess the subjects' experience in the virtual environment could be grouped into three factors: (1) presence in the virtual environment, (2) quality of the virtual environment, and (3) task difficulty.
A Complementary Column Generation Approach for the Graph Equipartition Problem
Al-Ykoob, Salem M.; Sherali, Hanif D. (2020-03-23)
This paper investigates the problem of partitioning a complete weighted graph into complete subgraphs, each having the same number of vertices, with the objective of minimizing the sum of edge weights of the resulting subgraphs. This NP-complete problem arises in many applications such as assignment and scheduling-related group partitioning problems and micro-aggregation techniques. In this paper, we present a mathematical programming model and propose a complementary column generation approach to solve the resulting model. A dual based lower bounding feature is also introduced to curtail the notorious tailing-off effects often induced when using column generation methods. Computational results are presented for a wide range of test problems.
A conceptual model of the sense of presence in virtual environments
Bystrom, Karl-Erik; Barfield, Woodrow S.; Hendrix, Claudia (MIT Press, 1999-04-01)
This paper proposes a model of interaction in virtual environments which we term the immersion, presence, performance (IPP) model. This model is based on previous models of immersion and presence proposed by Barfield and colleagues and Slater and colleagues. The IPP model describes the authors' current conceptualization of the effects of display technology, task demands, and attentional resource allocation on immersion, presence, and performance in virtual environments. The IPP model may be useful for developing a theoretical framework for research on presence and for interpreting the results of empirical studies on the sense of presence in virtual environments. The model may also be of interest to designers of virtual environments.
Conjoining Wymore’s Systems Theoretic Framework and the DEVS Modeling Formalism: Toward Scientific Foundations for MBSE
Wach, Paul; Zeigler, Bernard P.; Salado, Alejandro (MDPI, 2021-05-27)
The objective of this research article is to re-introduce some of the concepts provided by A. Wayne Wymore in his mathematical theory of Model-Based Systems Engineering, discuss why his framework might have not been adopted, and define a potential path to modernize the framework for practical application in the digital age. The dense mathematical theory has never been converted to a practical form. We propose a path to modernization by creating a metamodel of Wymore’s mathematical theory of MBSE. This enables explaining the concepts in simple to understand terms and shows the internal consistency provided by the theory. Furthermore, the metamodel allows for conversion of the theory into software application, for which we show some initial results that open the research to the art of the possible. In recognition of limitation of the theory, we make the case for a merger of the theoretical framework with the enhanced formalism of Discrete Event System Specification (DEVS). This will establish a path toward the scientific foundations for MBSE to enable future implementations of the complementary pairing and their empirical results.

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