Southern Methodist University
  • Dallas, TX, United States
Recent publications
In the era of big data, people benefit from the existence of tremendous amounts of information. However, availability of said information may pose great challenges. For instance, one big challenge is how to extract useful yet succinct information in an automated fashion. As one of the first few efforts, keyword extraction methods summarize an article by identifying a list of keywords. Many existing keyword extraction methods focus on the unsupervised setting, with all keywords assumed unknown. In reality, a (small) subset of the keywords may be available for a particular article. To use such information, we propose a rigorous probabilistic model based on a semisupervised setup. Our method incorporates the graph-based information of an article into a Bayesian framework via an informative prior so that our model facilitates formal statistical inference, which is often absent from existing methods. To overcome the difficulty arising from high-dimensional posterior sampling, we develop two Markov chain Monte Carlo algorithms based on Gibbs samplers and compare their performance using benchmark data. We use a false discovery rate (FDR)-based approach for selecting the number of keywords, whereas the existing methods use ad hoc threshold values. Our numerical results show that the proposed method compared favorably with state-of-the-art methods for keyword extraction. History: Accepted by Ramaswamy Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( ) as well as from the IJOC GitHub software repository ( ) at ( ).
Microorganisms are the primary engines of biogeochemical processes and foundational to the provisioning of ecosystem services to human society. Free‐living microbial communities (microbiomes) and their functioning are now known to be highly sensitive to environmental change. Given microorganisms' capacity for rapid evolution, evolutionary processes could play a role in this response. Currently, however, few models of biogeochemical processes explicitly consider how microbial evolution will affect biogeochemical responses to environmental change. Here, we propose a conceptual framework for explicitly integrating evolution into microbiome–functioning relationships. We consider how microbiomes respond simultaneously to environmental change via four interrelated processes that affect overall microbiome functioning (physiological acclimation, demography, dispersal and evolution). Recent evidence in both the laboratory and the field suggests that ecological and evolutionary dynamics occur simultaneously within microbiomes; however, the implications for biogeochemistry under environmental change will depend on the timescales over which these processes contribute to a microbiome's response. Over the long term, evolution may play an increasingly important role for microbially driven biogeochemical responses to environmental change, particularly to conditions without recent historical precedent.
Bupropion is widely used for the treatment of major depressive disorder and for smoking cessation assistance. Unfortunately, there are no practical systems to assist clinicians or poison centers in predicting outcomes based on clinical features. Hence, the purpose of this study was to use a decision tree approach to inform early diagnosis of outcomes secondary to bupropion overdose. This study utilized a dataset from the National Poison Data System, a six-year retrospective cohort study on toxic exposures and patient outcomes. A machine learning algorithm (decision tree) was applied to the dataset using the sci-kit-learn library in Python. Shapley Additive exPlanations (SHAP) were used as an explainable method. Comparative analysis was performed using Random Forest (RF), Gradient Boosting classification, eXtreme Gradient Boosting, Light Gradient Boosting (LGM), and Voting Ensembling. ROC curve and Precision-Recall curve were used to analyze the performance of each model. LGM and random forest demonstrated the highest performance to predict outcome of bupropion exposure. Multiple seizures, conduction disturbance, intentional exposure, and confusion were the most influential factors to predict the outcome of bupropion exposure. Coma and seizure, including single, multiple and status, were the most important factors to predict major outcomes.
The family systems theory and systemic framework of resilience suggest that immigrant mothers and children may show heterogeneous profiles of dyadic adaptation outcomes shaped by distinct adaptation resources. Thus, our study aimed to identify different adaptation patterns among 200 mother–child pairs of immigrants from Mainland China to Hong Kong. A dyadic latent profile analysis classified the immigrant mothers and children into four subgroups based on their well-being scores. As expected, the largest subgroup, labeled the adapted mothers and children subgroup (37%, Subgroup 1), reported high well-being in both the mothers and their children. Additionally, nearly 12% of mothers reported higher well-being whereas their children reported poorer well-being; this group was labeled the adapted mothers and maladapted children subgroup (Subgroup 2). In the third subgroup, labeled the maladapted mother and adapted children subgroup (34%, Subgroup 3), mothers reported poorer well-being but children reported higher well-being. Lastly, a subgroup including mothers and children with poorer adaptation (17%, Subgroup 4) was labeled the maladapted mothers and children subgroup. We also identified distinct configured patterns of contextual resources for each subgroup. Our findings highlight the importance of investigating the heterogeneous patterns of these immigrant mothers and children as well as the need to develop dyadic intervention programs to enhance positive adaptation.
Objective: Anxiety is highly prevalent in individuals with asthma. Asthma symptoms and medication can exacerbate anxiety, and vice versa. Unfortunately, treatments for comorbid anxiety and asthma are largely lacking. A problematic feature common to both conditions is hyperventilation. It adversely affects lung function and symptoms in asthma and anxiety. We examined whether a treatment to reduce hyperventilation, shown to improve asthma symptoms, also improves anxiety in asthma patients with high anxiety. Method: One-hundred-twenty English- or Spanish-speaking adult patients with asthma were randomly assigned to either capnometry-assisted respiratory training (CART) to raise PCO2 or feedback to slow respiratory rate (SLOW). Although anxiety was not an inclusion criterion, 21.7% met clinically-relevant anxiety levels on the Hospital Anxiety and Depression scale. Anxiety (HADS-A) and depression (HADS-D) scales, anxiety sensitivity (ASI), and negative affect (PANAS-N) were assessed at baseline, posttreatment,1-month follow-up, and 6-month follow-up. Results: In this secondary analysis, asthma patients with high baseline anxiety showed greater reductions in ASI and PANAS-N in CART than in SLOW (ps ≤ .005, Cohen's ds ≥ .58). Further, at 6-month follow-up, these patients also had lower ASI, PANAS-N, and HADS-D in CART than in SLOW (ps ≤ .012, Cohen's ds ≥ .54). Patients with low baseline anxiety did not have differential outcomes in CART than in SLOW. Conclusions: For asthma patients with high anxiety, our brief training designed to raise PCO2 resulted in significant and sustained reductions in anxiety sensitivity and negative affect compared to slow-breathing training. The findings lend support for PCO2 as a potential physiological target for anxiety reduction in asthma.Trial Identifier: NCT00975273.
The photosynthetic apparatus of plants and bacteria combine atomically precise pigment-protein complexes with dynamic membrane architectures to control energy transfer on the 10-100 nm length scales. Recently, synthetic materials have integrated photosynthetic antenna proteins to enhance exciton transport, though the influence of artificial packing on the excited-state dynamics in these biohybrid materials is not fully understood. Here, we use the adaptive hierarchy of pure states (adHOPS) to perform a formally exact simulation of excitation energy transfer within artificial aggregates of light-harvesting complex 2 (LH2) with a range of packing densities. We find that LH2 aggregates support a remarkable exciton diffusion length ranging from 100 nm at a biological packing density to 300 nm at the densest packing previously suggested in an artificial aggregate. The unprecedented scale of these formally exact calculations also underscores the efficiency with which adHOPS simulates excited-state processes in molecular materials.
In this paper, we present dyadic adaptive HOPS (DadHOPS), a new method for calculating linear absorption spectra for large molecular aggregates. This method combines the adaptive HOPS (adHOPS) framework, which uses locality to improve computational scaling, with the dyadic HOPS method previously developed to calculate linear and non-linear spectroscopic signals. To construct a local representation of dyadic HOPS, we introduce an initial state decomposition that reconstructs the linear absorption spectra from a sum over locally excited initial conditions. We demonstrate the sum over initial conditions can be efficiently Monte Carlo sampled and that the corresponding calculations achieve size invariant (i.e., O(1)) scaling for sufficiently large aggregates while trivially incorporating static disorder in the Hamiltonian. We present calculations on the photosystem I core complex to explore the behavior of the initial state decomposition in complex molecular aggregates, and as well as proof-of-concept DadHOPS calculations on an artificial molecular aggregate inspired by perylene bis-imide to demonstrate the size-invariance of the method.
The extreme conditions at the surface of Venus pose a challenge for monitoring the planet's seismic activity using long-duration landed probes. One alternative is using balloon-based sensors to detect venusquakes from the atmosphere. This study aims to assess the efficiency with which seismic motion is coupled as atmospheric acoustic waves across Venus's surface. It is, therefore, restricted to the immediate neighborhood of the crust-atmosphere interface. In order to account for supercritical conditions near the surface, the Peng-Robinson equation of state is used to obtain the acoustic sound speed and attenuation coefficient in the lower atmosphere. The energy transported across the surface from deep and shallow sources is shown to be a few orders of magnitude larger than on Earth, pointing to a better seismo-acoustic coupling. For a more realistic scenario, simulations were made of the acoustic field generated in the lower atmosphere by the ground motion arising from a vertical array of subsurface point-force sources. The resulting transmission loss maps show a strong epicentral cone accompanied by contributions from leaky surface waves. Results at 0.1 Hz and 1 Hz confirm that the width of the epicentral cone is larger at lower frequencies.
Both sides in the U.S. Civil War financed military spending by issuing new fiat currencies. The Union ‘greenback’ underwent moderate inflation (by wartime standards), but the Confederate ‘greyback’ suffered hyperinflation. Existing explanations for these price movements typically treat only one of the two cases and adopt either a quantity theory or rational expectations approach. We compare Union and Confederate policies directly and highlight the importance of taxation for assuring the value of inconvertible money. Combining monetary and fiscal history literatures, we find that tax policies were determined by long-term development of democratic governing institutions. Higher levels of democracy in the North, as compared to the slaveholding South, meant greater tax policy legitimacy and administrative competence. The Union drew on this legacy to back its money effectively, while the Confederacy failed to do so. We contribute to credit theories of money by drawing attention to the political determinants of effective fiscal policy.
Global warming is one of the top environmental concerns in the U.S, yet little is known about the effects of green advertising on different generational cohorts-Gen-Z, Gen-Y, Gen-X, and Baby Boomers. Specifically, environmental values (egoistic, altruistic, and biospheric) were assessed for differentially impacting ad attitudes and behavioral intentions across the four cohorts. Study-1 (n = 613) found the biospheric ad (plants and animals) to be more persuasive for Gen-Z and Gen-Y, with no preference shown for Gen-X and Baby Boomers, and no differences between the self (egoistic) and community (altruistic) focused ads. Based on these findings, Study 2 (n = 634) merged the four cohorts into two, Gen-ZY and Gen-XB, focused only on egoistic and biospheric, while introducing informativeness and task difficulty to better understand messaging preferences on attitudes and intentions to act. The findings revealed that Gen-ZY preferred to donate money for a biospheric ad with low information, while being motivated to volunteer their time when presented with a high information biospheric ad. Irrespective of ad appeal (egoistic or biospheric), Gen-XB had stronger intentions to donate for an advertisement with high information content (vs. low). Theoretical and advertiser implications are discussed along with future research avenues.
A compact and planar imaging system was developed using a flexible polymer substrate that can distinguish subcutaneous tissue abnormalities, such as breast tumors, based on electromagnetic-wave interactions in materials where permittivity variations affect wave reflection. The sensing element is a tuned loop resonator operating in the industrial, scientific, and medical (ISM) band at 2.423 GHz, providing a localized high-intensity electric field that penetrates into tissues with sufficient spatial and spectral resolutions. The resonant frequency shifts and magnitudes of the reflection coefficients indicate the boundaries of abnormal tissues under the skin due to their high contrasts to normal tissues. The sensor was tuned to the desired resonant frequency with a reflection coefficient of −68.8 dB for a radius of 5.7 mm, with a tuning pad. Quality factors of 173.1 and 34.4 were achieved in simulations and measurements in phantoms. An image-processing method was introduced to fuse raster-scanned 9 × 9 images of resonant frequencies and reflection coefficients for image-contrast enhancement. The results showed a clear indication of the tumor’s location at a depth of 15 mm and the capability to identify two tumors both at the depth of 10 mm. The sensing element can be expanded to a four-element phased array for deeper field penetration. Field analysis showed the depths of −20 dB attenuation were improved from 19 to 42 mm, giving wider coverage in tissues at resonance. Results showed that a quality factor of 152.5 was achieved and a tumor could be identified at a depth of up to 50 mm. In this work, simulations and measurements were conducted to validate the concept, showing great potential for subcutaneous imaging in medical applications in a noninvasive, efficient, and lower-cost way.
Predicting the viscosity of polymer nanocomposites (PNCs) is of critical importance as it governs a dominant role in PNCs processing and application. Machine learning (ML) algorithms, enabled by pre-existing experimental and computational data, have emerged as robust tools for the prediction of quantitative relationships between feature parameters and various physical properties of materials. In this work, we employed nonequilibrium molecular dynamics (NEMD) simulation with ML models to systematically investigate the viscosity of PNCs over a wide range of NPs loading, shear rates and temperature.With the increase in shear rates, shear thinning takes place as the value of viscosity decreases on the orders of magnitude. In addition, the NPs loading -dependence and T-dependence reduce to the extent that it is not visible at high shear rates. The value of viscosity for PNCs is proportional to NPs loading and inversely proportional to T below the intermediate shear rates. Using the obtained NEMD results, four machine learning models were trained to provides eective predictions for the viscosity. The extreme gradient boosting (XGBoost) model yields the best accuracy in viscosity prediction under complex conditions and is further used to evaluate feature importance. This quantitative structure-property relationship (QSPR) model used physical views to investigate the effect of process parameters, such as temperature, NPs loading and shear rates, on the viscosity of PNCs and paves the path for theoretically proposing reasonable parameters for successful processing.
Motivated from the increasing need to develop a science-based, predictive understanding of the dynamics and response of cities when subjected to natural hazards, in this paper, we apply concepts from statistical mechanics and microrheology to develop mechanical analogues for cities with predictive capabilities. We envision a city to be a matrix where cell-phone users are driven by the city’s economy and other incentives while using the collection of its infrastructure networks in a similar way that thermally driven Brownian particles are moving within a complex viscoelastic material. Mean-square displacements of thousands of cell-phone users are computed from GPS location data to establish the creep compliance and the resulting impulse response function of a city. The derivation of these time-response functions allows the synthesis of simple mechanical analogues that model satisfactorily the city’s behaviour under normal conditions. Our study concentrates on predicting the response of cities to acute shocks (natural hazards) that are approximated with a rectangular pulse; and we show that the derived solid-like mechanical networks predict that cities revert immediately to their pre-event response suggesting an inherent resilience. Our findings are in remarkable good agreement with the recorded response of the Dallas metroplex following the February 2021 North American winter storm.
The dominant approach to food security emphasizes food production and supply. This approach is too narrow. In reality, inputs are as important as actual food output. Without critical inputs, food production is constrained. Russia’s invasion of Ukraine illustrates the narrowness of the food production approach to food security. Russia is experiencing “input insecurity” which was revealed after its invasion of Ukraine. Specifically, the article explores Russia’s dependence on foreign farm machinery, seeds, and pedigree livestock. Russia’s input insecurity may not be long-term, but even so the dependence on foreign inputs means that Russia’s food security is not as secure as previously believed.
This article provides a critical appraisal of the March 2020 crisis in fixed income markets. We synthesize the main events, characterize what appears to be an emerging consensus on what caused the market breakdowns, summarize how the Federal Reserve's actions contributed to its resolution, and discuss potential lasting effects of the crisis. This review makes clearer the fragilities and interconnectedness that characterize the current fixed income market structure and their effects on liquidity provision. We argue that the current market structure, combined with the continued growth of Treasury markets, corporate and municipal bond markets, and particularly, mutual funds, raises the specter that periodic instability may remain a feature of fixed income markets. Expected final online publication date for the Annual Review of Financial Economics, Volume 15 is November 2023. Please see for revised estimates.
Aim: To determine the psychometric properties of the Digital Addiction Scale for Children. Design and method: This methodological study included 506 children aged 9-12 years. Data were collected using the child and family information form, the digital addiction scale for children and the digital game addiction scale for children. The data were evaluated using confirmatory factor analysis Cronbach's alpha, convergent validity, and gender-based measurement invariance analysis. Confirmatory factor analysis was applied using Mplus 8.7 with robust maximum likelihood estimation procedures. Results: Confirmatory factor were performed for construct validity. The scale was found to have good model fit indicators. The factor loadings of all the components were found to be >0.40. Convergent validity of Digital addiction scale for children and digital game addiction scale showed a significant positive high correlation. The total Cronbach alpha value of the scale was determined as 0.94, and the Cronbach alpha values of the subscales as Interpersonal 0.89, and Intrapersonal 0.91. Conclusion: The use of the digital addiction scale for children was determined to be a valid and reliable scale for the screening of digital device use and digital addiction in a Turkish sample. Practice implications: Nurses and other health professionals have an important role in detecting situations that put children's health at risk and promoting positive behaviors. It is especially important that school health nurses use valid and reliable tools that can determine children's digital addictions. Since this scale is easy and practical, it is thought that it will contribute significantly to the literature.
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3,862 members
Mehdi Mazar Atabaki
  • Department of Mechanical Engineering
Jun Gao
  • Department of Physics
Duncan Macfarlane
  • Department of Electrical Engineering
Prasanna Rangarajan
  • Department of Electrical Engineering
Neil Tabor
  • Department of Earth Sciences
12516 Audelia Rd, 75243, Dallas, TX, United States