Browsing by Author "Ranganathan, Shyam"

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    Advances in Stochastic Geometry for Cellular Networks
    Saha, Chiranjib (Virginia Tech, 2020-08-24)
    The mathematical modeling and performance analysis of cellular networks have seen a major paradigm shift with the application of stochastic geometry. The main purpose of stochastic geometry is to endow probability distributions on the locations of the base stations (BSs) and users in a network, which, in turn, provides an analytical handle on the performance evaluation of cellular networks. To preserve the tractability of analysis, the common practice is to assume complete spatial randomness} of the network topology. In other words, the locations of users and BSs are modeled as independent homogeneous Poisson point processes (PPPs). Despite its usefulness, the PPP-based network models fail to capture any spatial coupling between the users and BSs which is dominant in a multi-tier cellular network (also known as the heterogeneous cellular networks (HetNets)) consisting of macro and small cells. For instance, the users tend to form hotspots or clusters at certain locations and the small cell BSs (SBSs) are deployed at higher densities at these locations of the hotspots in order to cater to the high data demand. Such user-centric deployments naturally couple the locations of the users and SBSs. On the other hand, these spatial couplings are at the heart of the spatial models used in industry for the system-level simulations and standardization purposes. This dissertation proposes fundamentally new spatial models based on stochastic geometry which closely emulate these spatial couplings and are conductive for a more realistic and fine-tuned performance analysis, optimization, and design of cellular networks. First, this dissertation proposes a new class of spatial models for HetNets where the locations of the BSs and users are assumed to be distributed as Poisson cluster process (PCP). From the modeling perspective, the proposed models can capture different spatial couplings in a network topology such as the user hotspots and user BS coupling occurring due to the user-centric deployment of the SBSs. The PCP-based model is a generalization of the state-of-the-art PPP-based HetNet model. This is because the model reduces to the PPP-based model once all spatial couplings in the network are ignored. From the stochastic geometry perspective, we have made contributions in deriving the fundamental distribution properties of PCP, such as the distance distributions and sum-product functionals, which are instrumental for the performance characterization of the HetNets, such as coverage and rate. The focus on more refined spatial models for small cells and users brings to the second direction of the dissertation, which is modeling and analysis of HetNets with millimeter wave (mm-wave) integrated access and backhaul (IAB), an emerging design concept of the fifth generation (5G) cellular networks. While the concepts of network densification with small cells have emerged in the fourth generation (4G) era, the small cells can be realistically deployed with IAB since it solves the problem of high capacity wired backhaul of SBSs by replacing the last-mile fibers with mm-wave links. We have proposed new stochastic geometry-based models for the performance analysis of IAB-enabled HetNets. Our analysis reveals some interesting system-design insights: (1) the IAB HetNets can support a maximum number of users beyond which the data rate drops below the rate of a single-tier macro-only network, and (2) there exists a saturation point of SBS density beyond which no rate gain is observed with the addition of more SBSs. The third and final direction of this dissertation is the combination of machine learning and stochastic geometry to construct a new class of data driven network models which can be used in the performance optimization and design of a network. As a concrete example, we investigate the classical problem of wireless link scheduling where the objective is to choose an optimal subset of simultaneously active transmitters (Tx-s) from a ground set of Tx-s which will maximize the network-wide sum-rate. Since the optimization problem is NP-hard, we replace the computationally expensive heuristic by inferring the point patterns of the active Tx-s in the optimal subset after training a determinantal point process (DPP). Our investigations demonstrate that the DPP is able to learn the spatial interactions of the Tx-s in the optimal subset and gives a reasonably accurate estimate of the optimal subset for any new ground set of Tx-s.
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    Bayesian Dynamical Systems Modelling in the Social Sciences
    Ranganathan, Shyam; Spaiser, Viktoria; Mann, Richard P.; Sumpter, David J. T. (PLOS, 2014-01-20)
    Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.
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    The Cauchy-Net Mixture Model for Clustering with Anomalous Data
    Slifko, Matthew D. (Virginia Tech, 2019-09-11)
    We live in the data explosion era. The unprecedented amount of data offers a potential wealth of knowledge but also brings about concerns regarding ethical collection and usage. Mistakes stemming from anomalous data have the potential for severe, real-world consequences, such as when building prediction models for housing prices. To combat anomalies, we develop the Cauchy-Net Mixture Model (CNMM). The CNMM is a flexible Bayesian nonparametric tool that employs a mixture between a Dirichlet Process Mixture Model (DPMM) and a Cauchy distributed component, which we call the Cauchy-Net (CN). Each portion of the model offers benefits, as the DPMM eliminates the limitation of requiring a fixed number of a components and the CN captures observations that do not belong to the well-defined components by leveraging its heavy tails. Through isolating the anomalous observations in a single component, we simultaneously identify the observations in the net as warranting further inspection and prevent them from interfering with the formation of the remaining components. The result is a framework that allows for simultaneously clustering observations and making predictions in the face of the anomalous data. We demonstrate the usefulness of the CNMM in a variety of experimental situations and apply the model for predicting housing prices in Fairfax County, Virginia.
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    Contributions to Efficient Statistical Modeling of Complex Data with Temporal Structures
    Hu, Zhihao (Virginia Tech, 2022-03-03)
    This dissertation will focus on three research projects: Neighborhood vector auto regression in multivariate time series, uncertainty quantification for agent-based modeling networked anagrams, and a scalable algorithm for multi-class classification. The first project studies the modeling of multivariate time series, with the applications in the environmental sciences and other areas. In this work, a so-called neighborhood vector autoregression (NVAR) model is proposed to efficiently analyze large-dimensional multivariate time series. The time series are assumed to have underlying distances among them based on the inherent setting of the problem. When this distance matrix is available or can be obtained, the proposed NVAR method is demonstrated to provides a computationally efficient and theoretically sound estimation of model parameters. The performance of the proposed method is compared with other existing approaches in both simulation studies and a real application of stream nitrogen study. The second project focuses on the study of group anagram games. In a group anagram game, players are provided letters to form as many words as possible. In this work, the enhanced agent behavior models for networked group anagram games are built, exercised, and evaluated under an uncertainty quantification framework. Specifically, the game data for players is clustered based on their skill levels (forming words, requesting letters, and replying to requests), the multinomial logistic regressions for transition probabilities are performed, and the uncertainty is quantified within each cluster. The result of this process is a model where players are assigned different numbers of neighbors and different skill levels in the game. Simulations of ego agents with neighbors are conducted to demonstrate the efficacy of the proposed methods. The third project aims to develop efficient and scalable algorithms for multi-class classification, which achieve a balance between prediction accuracy and computing efficiency, especially in high dimensional settings. The traditional multinomial logistic regression becomes slow in high dimensional settings where the number of classes (M) and the number of features (p) is large. Our algorithms are computing efficiently and scalable to data with even higher dimensions. The simulation and case study results demonstrate that our algorithms have huge advantage over traditional multinomial logistic regressions, and maintains comparable prediction performance.
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    Does Rural Water System Design Matter? A Study of Productive Use of Water in Rural Nepal
    GC, Raj K.; Ranganathan, Shyam; Hall, Ralph P. (MDPI, 2019-09-23)
    In Nepal, rural water systems (RWS) are classified by practitioners as single-use domestic water systems (SUS) or multiple-use water systems (MUS). In the rural hills of Nepal, subsistence farming communities typically use RWS to support income-generating productive activities that can enhance rural livelihoods. However, there is limited research on the extent of existing productive activity and the factors enabling these activities. This paper examines the extent of water-related productive activities and the factors driving these activities based on a study, undertaken between October 2017 to June 2018, of 202 households served from five single-use domestic water systems and five multiple use water systems in the mid-hills of Nepal. The research found that a majority (94%) of these households engaged in two or more productive activities including growing vegetables and horticulture crops, raising livestock, and producing biogas and Rakshi (locally-produced alcohol), regardless of the system design, i.e., SUS vs. MUS. Around 90% of the households were engaged in productive activities that contributed to over 10% of their mean annual household income ($4,375). Since the SUS vs. MUS classification was not found to be a significant determinant of the extent of productive activity, the households were reclassified as having high or low levels of productive activity based on the quantity of water used for these activities and the associated earned income. A multinomial logistic regression model was developed to measure the relative significance of various predictors of high productive activity households. Five dominant predictors were identified: households that farm as a primary occupation, use productive technologies, are motivated to pursue productive activities, have received water-related productive activity training, and have received external support related to productive activities. Whereas MUS are designed for productive activity, nearly every household in SUS communities was involved in productive activities making them ‘de-facto’ MUS. These results challenge the current approach to rural water provision that views SUS and MUS as functionally different services.
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    The Dynamics of Democracy, Development and Cultural Values
    Spaiser, Viktoria; Ranganathan, Shyam; Mann, Richard P.; Sumpter, David J. T. (PLOS, 2014-06-06)
    Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values.
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    Ecological and Human Health in Rural Communities
    Gohlke, Julia M.; Kolivras, Korine N.; Krometis, Leigh-Anne H.; Marmagas, Susan West; Marr, Linsey C.; Satterwhite, Emily M.; Angermeier, Paul L.; Clark, Susan F.; Ranganathan, Shyam; Schoenholtz, Stephen H.; Swarup, Samarth; Thompson, Christopher K. (2017-05-15)
    Environmental exposures to chemicals and microbes in the air we breathe, the water we drink, the food we eat, and the objects we touch are now recognized to be responsible for 90% of all human illness. This suggests that well-documented health disparities within and between nations have significant geographic and ecological as well as socioeconomic dimensions that must be addressed in order to secure human well-being at local to global scales. While urbanization is a primary driver of global change, it is widely acknowledged that urbanization is dependent on large-scale resource extraction and agriculture in rural communities. Despite considerable evidence linking human industrial and agricultural activities to ecological health (i.e. health of an ecosystem including the non-human organisms that inhabit it), very little data are available directly linking exposure to environmental pollution and human health in rural areas, which have repeatedly been identified as subject to the most extreme health disparities...
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    Effects of an arm-support exoskeleton on perceived work intensity and musculoskeletal discomfort: An 18-month field study in automotive assembly
    Kim, Sunwook; Nussbaum, Maury A.; Smets, Marty; Ranganathan, Shyam (Wiley, 2021-08-06)
    Background: Exoskeleton (EXO) technologies are a promising ergonomic intervention to reduce the risk of work-related musculoskeletal disorders, with efficacy supported by laboratory- and field-based studies. However, there is a lack of field-based evidence on long-term effects of EXO use on physical demands. Methods: A longitudinal, controlled research design was used to examine the effects of arm-support exoskeleton (ASE) use on perceived physical demands during overhead work at nine automotive manufacturing facilities. Data were collected at five milestones (baseline and at 1, 6, 12, and 18 months) using questionnaires. Linear mixed models were used to understand the effects of ASE use on perceived work intensity and musculoskeletal discomfort (MSD). Analyses were based on a total of 41 participants in the EXO group and 83 in a control group. Results: Across facilities, perceived work intensity and MSD scores did not differ significantly between the EXO and control groups. In some facilities, however, neck and shoulder MSD scores in the EXO group decreased over time. Wrist MSD scores in the EXO group in some facilities remained unchanged, while those scores increased in the control group over time. Upper arm and low back MSD scores were comparable between the experimental groups. Conclusion: Longitudinal effects of ASE use on perceived physical demands were not found, though some suggestive results were evident. This lack of consistent findings is discussed, particularly supporting the need for systematic and evidence-based ASE implementation approaches in the field that can guide the optimal selection of a job for ASE use.
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    Exploring the Potential of Multiple Use Water Services for Smallholder Farmers in the Western Middle Hills of Nepal
    G.C., Raj Kumar (Virginia Tech, 2021-01-05)
    Rural water systems (RWS) are commonly used to provide water to households for domestic uses (drinking, cleaning, washing, and sanitation) in developing countries. Water supply practitioners often classify these systems as single-use water systems (SUS) or multiple-use water systems (MUS). Smallholder farming communities in rural western hills of Nepal typically use such systems for both domestic and income-generating productive activities (e.g., agriculture, livestock, dairy, bio-gas, Rakshi), regardless of whether they were designed for single or multiple water uses. Therefore, this research frames both systems as providing multiple-use water services that enhance the productive activity and livelihoods of small- holders. Little is known on the factors that influence the productive activity of households in the western middle hills of Nepal and the impact these activities have on the technical performance of water systems (measured by duration of system breakdowns). This research identifies the extent of water-related productive activities in rural Nepali households supported by single-use water systems (SUS) vs. multiple-use water systems (MUS), and explores the factors that enables households to engage in high-levels of productive activity. The vast majority of households were found to engage in small-scale productive activities no matter what the rural water systems were designed to support, and more than half of them earned an income from water-based activities. Households engaged in high-levels of productive activity farm as a primary occupation, use productive technologies, are motivated to pursue productive activities, have received water-related productive activity training, and have received external support related to productive activities. A multinomial regression was used to predict the factors associated with high levels of productive activities undertaken by small farms. A hierarchical regression model was then used to examine both household- and system-level variables that contribute to the breakdown of rural water systems, focusing on the duration of breakdowns. The predictors of water system breakdowns include social factors (household involvement in decision-making during water system planning and construction and a household's sense of ownership toward the water system), technical factors (the management capacity of the water user committee and activity level of the system operator), economic factors (income earned from water-based productive activities), and geographic factors (the distance from the village to the water source). Finally, a conceptual model was developed to help identify strategies for implementing and scaling-up MUS. Scaling-up strategies for MUS begin with community participation in lo- cal government planning and budgeting. Under a new Constitution that went into effect in January 2017, newly formed local governments are to be provided with the funding and budget authority to determine local service priorities and how these services will be funded, designed, and implemented. The scaling-up of MUS would require local government officials, water system users, and private actors to develop the technical and institutional capacity needed to build and manage MUS, including the many support services needed by small- holder growers to realize its benefits. This research also examines the potential approaches that could enable subsistence farmers to become viable commercial producers. While growers are typically risk-adverse producers, this research identifies the mediating factors that could expand the long-term engagement of these producers in commercial agricultural production. These factors include adequate access to year-round irrigation, the use of improved production technologies and practices, improved access to rural markets, and improved production skills. The findings of this research will also be of value to Governmental, Non-Governmental Organizations (NGOs) and private sector actors who are looking to effectively mobilize their resources and expertise in support of smallholder farming in the hills of Nepal.
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    A General Micro-Level Modeling Approach to Analyzing Interconnected SDGs: Achieving SDG 6 and More through Multiple-Use Water Services (MUS)
    Hall, Ralph P.; Ranganathan, Shyam; GC, Raj K. (MDPI, 2017-02-21)
    The 2030 agenda presents an integrated set of Sustainable Development Goals (SDGs) and targets that will shape development activities for the coming decade. The challenge now facing development organizations and governments is how to operationalize this interconnected set of goals and targets through effective projects and programs. This paper presents a micro-level modeling approach that can quantitatively assess the impacts associated with rural water interventions that are tailored to specific communities. The analysis focuses on how a multiple-use water services (MUS) approach to SDG 6 could reinforce a wide range of other SDGs and targets. The multilevel modeling framework provides a generalizable template that can be used in multiple sectors. In this paper, we apply the methodology to a dataset on rural water services from Mozambique to show that community-specific equivalents of macro-level variables used in the literature such as Cost of Illness (COI) avoided can provide a better indication of the impacts of a specific intervention. The proposed modeling framework presents a new frontier for designing projects in any sector that address the specific needs of communities, while also leveraging the knowledge gained from previous projects in any country. The approach also presents a way for agencies and organizations to design projects or programs that bridge sectors/disciplines (water, irrigation, health, energy, economic development, etc.) to advance an interconnected set of SDGs and targets.
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    Maternal proximity to Central Appalachia surface mining and birth outcomes
    Buttling, Lauren G.; McKnight, Molly Xi; Kolivras, Korine N.; Ranganathan, Shyam; Gohlke, Julia M. (Wolters Kluwer Health, 2021-02)
    Maternal residency in Central Appalachia counties with coal production has been previously associated with increased rates of low birth weight (LBW). To refine the relationship between surface mining and birth outcomes, this study employs finer spatiotemporal estimates of exposure.

    Methods

    We developed characterizations of annual surface mining boundaries in Central Appalachia between 1986 and 2015 using Landsat data. Maternal address on birth records was geocoded and assigned amount of surface mining within a 5 km radius of residence (street-level). Births were also assigned the amount of surface mining within residential ZIP code tabulation area (ZCTA). Associations between exposure to active mining during gestation year and birth weight, LBW, preterm birth (PTB), and term low birth weight (tLBW) were determined, adjusting for outcome rates before active mining and available covariates.

    Results

    The percent of land actively mined within a 5 km buffer of residence (or ZCTA) was negatively associated with birth weight (5 km: β = -14.07 g; 95% confidence interval [CI] = -19.35, -8.79, P = 1.79 × 10-7; ZCTA: β = -9.93 g; 95% CI = -12.54, -7.33, P = 7.94 × 10-14). We also found positive associations between PTB and active mining within 5 km (odds ratio [OR] = 1.06; 95% CI = 1.03, 1.09, P = 1.43 × 10-4) and within ZCTA (OR = 1.04; 95% CI = 1.03, 1.06, P = 9.21 × 10-8). Positive relationships were also found between amount of active mining within 5 km or ZIP code of residence and LBW and tLBW outcomes.

    Conclusions

    Maternal residency near active surface mining during gestation may increase risk of PTB and LBW.
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    Multi‑level modeling with nonlinear movement metrics to classify self‑injurious behaviors in autism spectrum disorder
    Cantin‑Garside, Kristine D.; Srinivasan, Divya; Ranganathan, Shyam; White, Susan W.; Nussbaum, Maury A. (Nature Research, 2020)
    Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods. Sensor-based behavioral monitoring could address the limitations of these methods, though the complex problem of classifying variable behavior should be addressed first. We aimed to address this need by developing a group-level model accounting for individual variability and potential nonlinear trends in SIB, as a secondary analysis of existing data. Ten participants with ASD and SIB engaged in free play while wearing accelerometers. Movement data were collected from > 200 episodes and 18 different types of SIB. Frequency domain and linear movement variability measures of acceleration signals were extracted to capture differences in behaviors, and metrics of nonlinear movement variability were used to quantify the complexity of SIB. The multi-level logistic regression model, comprising of 12 principal components, explained > 65% of the variance, and classified SIB with > 75% accuracy. Our findings imply that frequency-domain and movement variability metrics can effectively predict SIB. Our modeling approach yielded superior accuracy than commonly used classifiers (~75 vs. ~ 64% accuracy) and had superior performance compared to prior reports (~75 vs. ~ 69% accuracy) This work provides an approach to generating an accurate and interpretable group-level model for SIB identification, and further supports the feasibility of developing a real-time SIB monitoring system.
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    Optimal Driver Risk Modeling
    Mao, Huiying (Virginia Tech, 2019-08-21)
    The importance of traffic safety has prompted considerable research on predicting driver risk and evaluating the impact of risk factors. Driver risk modeling is challenging due to the rarity of motor vehicle crashes and heterogeneity in individual driver risk. Statistical modeling and analysis of such driver data are often associated with Big Data, considerable noise, and lacking informative predictors. This dissertation aims to develop several systematic techniques for traffic safety modeling, including finite sample bias correction, decision-adjusted modeling, and effective risk factor construction. Poisson and negative binomial regression models are primary statistical analysis tools for traffic safety evaluation. The regression parameter estimation could suffer from the finite sample bias when the event frequency (e.g., the total number of crashes) is low, which is commonly observed in safety research. Through comprehensive simulation and two case studies, it is found that bias adjustment can provide more accurate estimation when evaluating the impacts of crash risk factors. I also propose a decision-adjusted approach to construct an optimal kinematic-based driver risk prediction model. Decision-adjusted modeling fills the gap between conventional modeling methods and the decision-making perspective, i.e., on how the estimated model will be used. The key of the proposed method is to enable a decision-oriented objective function to properly adjust model estimation by selecting the optimal threshold for kinematic signatures and other model parameters. The decision-adjusted driver-risk prediction framework can outperform a general model selection rule such as the area under the curve (AUC), especially when predicting a small percentage of high-risk drivers. For the third part, I develop a Multi-stratum Iterative Central Composite Design (miCCD) approach to effectively search for the optimal solution of any "black box" function in high dimensional space. Here the "black box" means that the specific formulation of the objective function is unknown or is complicated. The miCCD approach has two major parts: a multi-start scheme and local optimization. The multi-start scheme finds multiple adequate points to start with using space-filling designs (e.g. Latin hypercube sampling). For each adequate starting point, iterative CCD converges to the local optimum. The miCCD is able to determine the optimal threshold of the kinematic signature as a function of the driving speed.
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    Precision Aggregated Local Models
    Edwards, Adam Michael (Virginia Tech, 2021-01-28)
    Large scale Gaussian process (GP) regression is infeasible for larger data sets due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide-and-conquer, e.g., partitioning into sub-problems and inducing functional (and thus computational) independence. Such approximations can speedy, accurate, and sometimes even more flexible than an ordinary GPs. However, a big downside is loss of continuity at partition boundaries. Modern methods like local approximate GPs (LAGPs) imply effectively infinite partitioning and are thus pathologically good and bad in this regard. Model averaging, an alternative to divide-and-conquer, can maintain absolute continuity but often over-smooth, diminishing accuracy. Here I propose putting LAGP-like methods into a local experts-like framework, blending partition-based speed with model-averaging continuity, as a flagship example of what I call precision aggregated local models (PALM). Using N_C LAGPs, each selecting n from N data pairs, I illustrate a scheme that is at most cubic in n, quadratic in N_C, and linear in N, drastically reducing computational and storage demands. Extensive empirical illustration shows how PALM is at least as accurate as LAGP, can be much faster in terms of speed, and furnishes continuous predictive surfaces. Finally, I propose sequential updating scheme which greedily refines a PALM predictor up to a computational budget, and several variations on the basic PALM that may provide predictive improvements.
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    Setting development goals using stochastic dynamical system models
    Ranganathan, Shyam; Nicolis, Stamatios C.; Swain, Ranjula Bali; Sumpter, David J. T. (PLOS, 2017-02-27)
    The Millennium Development Goals (MDG) programme was an ambitious attempt to encourage a globalised solution to important but often-overlooked development problems. The programme led to wide-ranging development but it has also been criticised for unrealistic and arbitrary targets. In this paper, we show how country-specific development targets can be set using stochastic, dynamical system models built from historical data. In particular, we show that the MDG target of two-thirds reduction of child mortality from 1990 levels was infeasible for most countries, especially in sub-Saharan Africa. At the same time, the MDG targets were not ambitious enough for fast-developing countries such as Brazil and China. We suggest that model-based setting of country-specific targets is essential for the success of global development programmes such as the Sustainable Development Goals (SDG). This approach should provide clear, quantifiable targets for policymakers.
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    Virginia Tech Food Access and Security Study
    Hall, Ralph P.; Ranganathan, Shyam; Agnew, Jessica L.; Christie, Maria Elisa; Kirk, Gary R.; Lucero, Christian; Clark, Susan F.; Archibald, Thomas G. (Virginia Tech, 2019-10-30)
    There is growing evidence to suggest that a substantial number of college and university students in the United States grapple with food insecurity during their studies. One of the most comprehensive surveys on this issue was conducted by The Hope Center with 33 participating four-year institutions. They estimated that 41% of students had low or very low food security (Goldrick-Rab et al. 2019). A review of food security studies by the U.S. Government Accountability Office (GAO) (2018) found similar results and that few students who qualified for food assistance were aware of federal food assistance programs such as the Supplemental Nutritional Assistance Program (SNAP). In response to the increasing concern over students’ access to food, this study aims to document food security at Virginia Tech. The study was designed with two parallel goals: to contribute to the national conversation on food access and security amongst higher education students; and to inform a strategic response through data-informed programs and policies at Virginia Tech. The first phase of the study was conducted between Fall 2017 and Spring 2018 and consisted of semi-structured key informant interviews. The second phase was conducted between December 2018 and January 2019 and consisted of an anonymous survey distributed to 32,242 students (27,421 undergraduate and 4,821 graduate) located in Blacksburg. A total of 2,441 (8.9%) undergraduate and 589 (12.2%) graduate students completed the entire survey (for a combined response rate of 9.4%). This study finds that 29% (±3.8%) of undergraduate and 35% (±7%) of graduate students were classified as having low or very low food security based on the USDA food security instrument. These findings are comparable with The Hope Center study (Goldrick-Rab et al. 2019). Students with low/very low food security status were more likely to be Hispanic/Latino or Black/African American, be receiving a Pell grant or financing their education through sources that need to be repaid, have a low GPA, and/or have a disability. Graduate students were also more likely than undergraduate students to be unable to afford to eat balanced meals or have to cut the size of their meals due to a lack of available funds. In general, the proportion of graduate students experiencing food-access problems was greater than the proportion of undergraduate students. A diet diversity score (DDS) was also developed from the student survey to measure the foods consumed by an individual within the previous 24 hours. The DDS is a proxy for dietary quality and helps provide insight into the barriers that students might face in accessing nutritious foods. The study found that on average students classified as having low/very low food security also had a lower DDS. This finding confirms that low food security is associated with a lower diet quality in addition to not having access to enough food. Students who reported that they sometimes or often did not have enough to eat in the past 12 months were also asked if they have received benefits from a range of food assistance programs. Of the 219 students who were asked this question, only 9% (n=20) reported receiving some form of assistance. When asked why they had not used a food assistance program, the primary response was that they felt other people needed more assistance than they did. The next three most selected reasons were a lack of awareness about (1) whether they were eligible for a food assistance program, (2) what programs exist, and (3) whom to speak with about what resources are available. These findings are consistent with the GAO (2018) report. These results reveal that students who potentially need food assistance may not know where to look for help, and administrative and/or social barriers related to existing on- and off-campus services may prevent students from seeking help even if they know it is available. This report also documents a range of on- and off-campus food assistance services that are available for students and provides a summary of the feedback obtained from the key informant interviews on potential next steps that could be taken by Virginia Tech. These steps include enhancing the coordination among, and awareness of, existing food assistance programs on and off campus, and new ideas such as creating an on-campus food pantry or subsidizing the cost of dining for students in need. Regardless of which actions are taken, we believe this report reveals our collective responsibility to ensure that no student at Virginia Tech goes hungry or is unable to access nutritious foods, and to create a community that nurtures learning and growth for all of its members.
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    What factors determine the technical performance of community-managed rural water systems in the middle hills of Nepal?
    Raj, K. G. C.; Ranganathan, Shyam; Hammett, A. L. (Tom); Hall, Ralph P. (2021-03)
    Gravity-fed water systems are widely used in the rural hills of Nepal. This study identifies the systematic factors that contribute to rural households not obtaining water due to system breakdowns. The study makes use of data from a 2017 to 2018 study of 202 households served by 10 community-based water systems from three localities within the western middle hills of Nepal. A hierarchical regression model is used to capture both household- and system-level variables. The analysis identifies three household-level and three system-level predictors of the duration of water system breakdowns. The significant household-level predictors include (1) a sense of ownership toward the water system, (2) user involvement in decision making during the planning and implementation of the water system, and (3) income earned from water-based productive activities. The significant system-level predictors include (1) distance from the village to the water source, (2) the performance of the water user committee, and (3) the water system operator's level of activity. In addition, the interactions between household- and system-level variables are captured. The empirical relationship between household productive income and the duration of breakdowns is a novel finding. These findings will be valuable to the Nepalese government and other actors working to implement sustainable water systems.
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    Wildfire as Coupled Human Natural System
    Farkhondehmaal, Farshad (Virginia Tech, 2022-02-01)
    Wildfire activity has increased in recent years in the United States, endangering both environment and society. Appropriate management of this phenomenon is only achievable with a thorough understanding of the critical factors influencing wildfire activity in each region. In three essays, I use statistical and mathematical models to examine wildfires and propose solutions to mitigate their impact on society. In the first essay, I focused on building a systematic framework for modeling wildfire as a coupled human-natural system. I employ system dynamics modeling, which was previously applied in various fields, including healthcare, sustainability, and disaster mitigation. I show how, in the absence of exogenous factors such as temperature or lightning, the human perception of fire danger may establish a feedback loop that can yield significant trends such as fluctuation or even fluctuation with rising amplitude when linked with the natural system. This conclusion is counter-intuitive, given that the human contribution to wildfire is typically described in the literature using constant or semi-constant variables. Additionally, I analyzed the impact of three important fire protection measures on reducing burning rates (prescribed burning, enhancing immediate suppression accomplishment, and regulating the rate of WUI growth). The research concludes that appropriately integrating several policies can result in a synergistic effect that is greater than the sum of the effects of the individual policies. The second essay calibrates the model built in the first essay and examines wildfire trends across the contiguous United States. The simulation results closely match the real data, and the model serves as a foundation for data-driven policy research. To be more precise, I fit the model to each state separately and then compare the model's goodness of fit. Following that, I examine the influence of various policies and scenarios on wildfire behavior. In the scenario, I examine the effect of maintaining constant temperatures and precipitation levels relative to the average values for these variables over the last century. For the policy analysis, I examine the influence of three policies on each state (prescribed burning, increasing immediate suppression achievement, and regulating the rate of WUI development). Here, I provide state-specific suggestions about the primary factors that contribute to wildfires and the most effective policies for each state. In the third essay, I have implemented the Oregon wildfire history dataset and integrated it with two other aerial datasets, including meteorological data gathered by weather stations located around the state and counties. Then, using hierarchical modeling on over 10,000 wildfire ignitions, I developed a classification system for determining if a given fire has the potential to grow major or not. However, utilizing a huge dataset and a variety of resources presents several obstacles, such as the presence of missing data. I imputed the missing numbers using a sophisticated mathematical approach called "Predictive Mean Matching".