Middle Tennessee State University
  • Murfreesboro, Tennessee, United States
Recent publications
Building on the self-determination theory and technology acceptance model, this study considers tourists’ adoption decisions around ridesharing and taxi services and examines whether actual rideshare use on-site affects visitors’ trip-level (i.e., trip satisfaction and trip value) and destination-level (i.e., locals’ perceived friendliness) evaluations. Econometric analysis is used to empirically evaluate actual behavioral data of domestic tourists collected from a U.S.-wide household tourism survey with 11,282 valid respondents in 2016 and 6,955 in 2020. Results indicate that while consumption competency, peer-to-peer consumption spillover, and taxi service availability significantly influenced tourists’ use of ridesharing services, safety reputation and destination familiarity are not significant determinants. Over time, cost saving is no longer significant in explaining the ridesharing demand, and during the pandemic, local pandemic severity shaped tourists’ use of ridesharing. Notably, tourists’ rideshare use significantly affected their trip- or destination-level assessments in 2020 but not in 2016. Theoretical and managerial implications are discussed.
A quadrangular embedding of a graph in a surface Σ, also known as a quadrangulation of Σ, is a cellular embedding in which every face is bounded by a 4-cycle. A quadrangulation of Σ is minimal if there is no quadrangular embedding of a (simple) graph of smaller order in Σ. In this paper we determine n(Σ), the order of a minimal quadrangulation of a surface Σ, for all surfaces, both orientable and nonorientable. Letting S0 denote the sphere and N2 the Klein bottle, we prove that n(S0)=4,n(N2)=6, and n(Σ)=⌈(5+25−16χ(Σ))/2⌉ for all other surfaces Σ, where χ(Σ) is the Euler characteristic. Our proofs use a ‘diagonal technique’, introduced by Hartsfield in 1994. We explain the general features of this method.
Whole class discussions (WCDs) are an important pedagogical tool for mathematics classes but are challenging to characterize across large numbers of observations because of their dynamic and complex nature. In this paper, we report on an exploratory method to characterize WCDs in mathematics classes across large numbers of observations that we refer to as Conversation Profile Analysis (CPA). CPA uses Latent Class Modeling (LCM) with live observation data to generate profiles of WCDs in middle-grade mathematics classes. We report on our exploratory use of CPA to analyze observation data from 259 WCDs about data and statistics in middle school classes making use of an innovative approach to instruction called Data Modeling. We identified 4 profiles of WCDs and found that these profiles varied in likelihood across time and were associated with different ways students talked about key mathematical ideas. We also discuss broader implications of the CPA approach to studying WCDs in math classes.
Understanding how host-associated microbial assemblages respond to pathogen invasion has implications for host health. Until recently, most investigations have focused on understanding the taxonomic composition of these assemblages. However, recent studies have suggested that microbial assemblage taxonomic composition is decoupled from its function, with assemblages being taxonomically varied but functionally constrained. The objective of this investigation was to understand how the Tri-colored bat, Perimyotis subflavus cutaneous microbial assemblage responds to fungal pathogen invasion within a functional context. We hypothesized that at a broad scale (e.g., KEGG pathways), there will be no difference in the functional assemblages between the white nose pathogen, Pseudogymnoascus destructans, positive and negative bats; and this pattern will be driven by the functional redundancy of bacterial taxa. At finer scales (e.g., gene models), we postulate differences in function attributed to interactions between bacteria and P. destructans, resulting in the production of antifungal metabolites. To test this, we used a combination of shotgun metagenomic and amplicon sequencing to characterize the bat cutaneous microbial assemblage in the presence/absence of P. destructans. Results showed that while there was a shift in taxonomic assemblage composition between P. destructans positive and negative bats, there was little overall difference in microbial function. Functional redundancy across bacterial taxa was clear at a broad-scale; however, both redundancy and variation in bacterial capability related to defense against pathogens was evident at finer scales. While functionality of the microbial assemblage was largely conserved in relation to P. destructans, the roles of particular functional pathways in resistance to fungal pathogens require further attention.
A rapidly growing crime problem in the United States and abroad, package theft is a phenomenon that is of persistent and significant concern to the public. However, the academic study on the subject has been neglected. Because package theft is understudied, the impact it has is difficult to understand, and the crime itself is hard to address. This present study defines package theft and informs readers about package theft and examines online retail consumers’ fear of the crime. Fear of crime research has typically focused on the fear of violent crime, or it has concentrated on an abstract fear of crime. This study broadens the literature about fear of crime by examining fear of a specific crime, package theft, using an index of eleven questions measuring behavioral, cognitive, and affective impacts. Survey data of 562 participants from 49 states reveal that nearly one quarter (23.8%) have experienced package theft. Fear of package theft was statistically significant among the variables gender (women more than men), residential location (urban and suburban more than rural), and previous victimization of package theft. Additionally, the fear of package theft directly or vicariously harmed consumers and resulted in risk mitigation and risk avoidance behaviors. The present study demonstrates the usefulness of measuring fear through an index of multiple variables, for a specific crime, within an environmental context.
The selection and breeding of deep rooting and drought-tolerant varieties has become a promising approach for improving the yield and adaptability of potato (Solanum tuberosum L.) in arid and semiarid areas. Therefore, the discovery of root-development-related genes and drought tolerance signaling pathways in potato is important. In this study, we used deep-rooting (C119) and shallow-rooting (C16) potato genotypes, with different levels of drought tolerance, to achieve this objective. Both genotypes were treated with 150 mM mannitol for 0 h (T0), 2 h (T2), 6 h (T6), 12 h (T12), and 24 h (T24), and their root tissues were subjected to comparative transcriptome analysis. A total of 531, 1571, 1247, and 3540 differentially expressed genes (DEGs) in C16 and 1531, 1108, 674, and 4850 DEGs in C119 were identified in T2 vs. T0, T6 vs. T2, T12 vs. T6, and T24 vs. T12 comparisons, respectively. Gene expression analysis indicated that a delay in the onset of drought-induced transcriptional changes in C16 compared with C119. Functional enrichment analysis revealed genotype-specific biological processes involved in drought stress tolerance. The metabolic pathways of plant hormone transduction and MAPK signaling were heavily involved in the resistance of C16 and C119 to drought, while abscisic acid (ABA), ethylene, and salicylic acid signal transduction pathways likely played more important roles in C119 stress responses. Furthermore, genes involved in root cell elongation and division showed differential expression between the two genotypes under drought stress. Overall, this study provides important information for the marker-assisted selection and breeding of drought-tolerant potato genotypes.
The crack growth of transparent materials after laser wavelength irradiation was studied. It is known that laser irradiation is used in many applications for the ablation of undesired material and/or coatings. The impact of laser irradiation on cracks was studied using the digital holography (DH) technique. Transparent samples were irradiated using near-ultraviolet, visible, near-infrared, and infrared light. The DH system is able to detect cracks and crack growth of the transparent samples irradiated by a range of laser wavelengths. Results also show that light with infrared to near-infrared wavelengths has a great effect on crack growth. High-resolution photomechanical effects of laser irradiation on material expansion or/and generation of defects due to specific wavelengths are also illustrated. The DH system with a multispectral laser has practical applications for laser cleaning of painted artworks.
Due to the COVID-19 pandemic in Shanghai, China, all school classes were delivered through an online environment from February 24 to May 22, 2020. To support this transition, the Shanghai Education Commission led expert teachers and specialists to develop a series of online video lessons based on the Shanghai unified curriculum, and suggested students watch the online video lessons individually from home, followed by an online synchronous lesson supported by class teachers. This study investigated what primary mathematics teachers learned from addressing these challenges through a case study. By following two purposefully selected teachers over 2 weeks during the transition, multiple data sets including online video lessons, online synchronous lessons, daily reflections, and post-online teacher interviews were collected. A fine-grained analysis of the data from the lens of the documentational approach to didactics found that teachers adaptively used online video lessons as important resources for their online synchronous lessons and virtual Teaching Research Groups as a teachers' collaboration mechanism supported them to develop online video lessons and address various technological constraints. Finally, implications of this case study for mathematics education globally are discussed. Supplementary information: The online version contains supplementary material available at 10.1007/s10649-022-10172-2.
Various knowledge sources have been hypothesized to relate to individual differences in reading comprehension skill in developing readers. We present results from two studies using explanatory item-response models to examine the unique role of knowledge in predicting reading and listening comprehension in 5th grade students (mean age of 10.77 years). In study 1, we investigated the importance of different knowledge sources for comprehending grade-level passages. Participants were 254 students with a range of reading abilities. We found that passage-specific topic familiarity, general academic knowledge, and vocabulary knowledge were all significantly associated with the probability of correctly answering questions about a passage. In study 2, we examined the possible transfer mechanisms that allow knowledge in one area to influence comprehension in a related but unfamiliar area. Participants were 26 students embedded in an Interactive Humanities course focusing on the Renaissance period. Students listened to parallel passages on Guttenberg and the printing press and Twitter use in the Arab Spring and answered comprehension questions. The probability of answering a question about the novel Twitter passage was significantly predicted by the ability to answer the corresponding question on the familiar printing press passage. Results point to the importance of knowledge sources in accounting for variance in comprehension performance.
LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems.
The present study evaluated the individual and combined effects of coated and uncoated phytase on broiler performance, tibia characteristics, and residual phytate phosphorus (P) in manure. Two repeated studies were conducted using 240-day-old Cobb 500 by-product male broilers per trial. For each trial, birds were assigned to four treatments with four replicate battery cages per treatment (60 birds/trt) and grown for 21 days. Treatments included: (1) negative control (NC), (2) NC + 1000 phytase units (FTU) coated phytase (C), (3) NC + 1000 FTU uncoated phytase (U), and (4) NC + 500 FTU coated + 500 FTU uncoated phytase (CU). Data were analyzed with a one-way ANOVA and means were separated using Tukey’s HSD. In the pooled data for both trials, all treatments with dietary phytase had a higher body weight (BW) and feed consumption (FC) than the NC on day 21 (p < 0.05). Similarly, a six-point reduction was observed for day 1 to 21 feed conversion (FCR) for U and CU (p < 0.05). All treatments with phytase inclusion differed from the NC in every evaluated parameter for bone mineralization (p < 0.05) and had significantly lower fecal phytate P concentrations compared to the NC (p < 0.05). Overall, bird performance was essentially unaffected by phytase form, indicating that combining phytase forms does not appear to offer any advantage to the evaluated parameters from day 1 to 21.
Performing Markov chain Monte Carlo parameter estimation on complex mathematical models can quickly lead to endless searching through highly multimodal parameter spaces. For computationally complex models, one rarely has prior knowledge of the optimal proposal distribution. In such cases, the Markov chain can become trapped near a suboptimal mode, lowering the computational efficiency of the method. With these challenges in mind, we present a novel MCMC kernel which incorporates both mixing and adaptation. The method is flexible and robust enough to handle parameter spaces that are highly multimodal. Other advantages include not having to locate a near-optimal mode with a different method beforehand, as well as requiring minimal computational and storage overhead from standard Metropolis. Additionally, it can be applied in any stochastic optimization context which uses a Gaussian kernel. We provide results from several benchmark problems, comparing the kernel's performance in both optimization and MCMC cases. For the former, we incorporate the kernel into a simulated annealing method and real-coded genetic algorithm. For the latter, we incorporate it into the standard Metropolis and adaptive Metropolis methods.
Exponential integrators, due to their robust stability properties, have been considered as reliable schemes for numerical solutions of stiff systems. In this paper, we propose generalized exponential time differencing (GETD) schemes for nonlinear fractional differential equations of order α ∈ (0,1). First, we improve the suboptimal performance of the multistep GETD schemes. Using graded mesh, uniform optimal convergence rates under no additional smoothness requirements are obtained. Second, we develop and analyze novel second-order and third-order accurate predictor-corrector type GETD schemes. Using linear stability analysis and numerical illustrations, we demonstrate that the newly introduced schemes have better stability properties than the multistep GETD schemes. Partial fraction decompositions of global Padé approximations for Mittag-Leffler function are used for efficient implementation. Numerical examples involving nonlinear scalar equations and stiff systems are provided to illustrate the theoretical findings.
The visual appearance of the fish fillet is a significant determinant of consumers’ purchase decisions. Depending on the rainbow trout diet, a uniform bright white or reddish/pink fillet color is desirable. Factors affecting fillet color are complex, ranging from the ability of live fish to accumulate carotenoids in the muscle to preharvest environmental conditions, early postmortem muscle metabolism, and storage conditions. Identifying genetic markers of fillet color is a desirable goal but a challenging task for the aquaculture industry. This study used weighted, single-step GWAS to explore the genetic basis of fillet color variation in rainbow trout. We identified several SNP windows explaining up to 3.5%, 2.5%, and 1.6% of the additive genetic variance for fillet redness, yellowness, and whiteness, respectively. SNPs are located within genes implicated in carotenoid metabolism (β,β-carotene 15,15′-dioxygenase, retinol dehydrogenase) and myoglobin homeostasis (ATP synthase subunit β, mitochondrial (ATP5F1B)). These genes are involved in processes that influence muscle pigmentation and postmortem flesh coloration. Other identified genes are involved in the maintenance of muscle structural integrity (kelch protein 41b (klh41b), collagen α-1(XXVIII) chain (COL28A1), and cathepsin K (CTSK)) and protection against lipid oxidation (peroxiredoxin, superoxide dismutase 2 (SOD2), sestrin-1, Ubiquitin carboxyl-terminal hydrolase-10 (USP10)). A-to-G single-nucleotide polymorphism in β,β-carotene 15,15′-dioxygenase, and USP10 result in isoleucine-to-valine and proline-to-leucine non-synonymous amino acid substitutions, respectively. Our observation confirms that fillet color is a complex trait regulated by many genes involved in carotenoid metabolism, myoglobin homeostasis, protection against lipid oxidation, and maintenance of muscle structural integrity. The significant SNPs identified in this study could be prioritized via genomic selection in breeding programs to improve fillet color in rainbow trout.
This work is a unique integration of three different areas, including smart eye status monitoring, supply chain operations reference (SCOR), and system dynamics, to explore the dynamics of the supply chain network of smart eye/vision monitoring systems. Chronic eye diseases such as glaucoma affect millions of individuals worldwide and, if left untreated, can lead to irreversible vision loss. Nearly half of the affected population is unaware of the condition and can be informed with frequent, accessible eye/vision tests. Tonometry is the conventional clinical method used in healthcare settings to determine the intraocular pressure (IOP) level for evaluating the risk of glaucoma. There are currently very few (under development) non-contact and non-invasive methods using smartphones to determine the risk of IOP and/or the existence of other eye-related diseases conveniently at home. With the overall goal of improving health, well-being, and sustainability, this paper proposes Eye-SCOR: a supply chain operations reference (SCOR)-based framework to evaluate the effectiveness of smartphone-based eye status monitoring apps. The proposed framework is designed using system dynamics modeling as a subset of a new causal model. The model includes interaction/activities between the main players and enablers in the supply chain network, namely suppliers/service providers, smartphone app/device factors, customers, and healthcare professionals, as well as cash and information flow. The model has been tested under various scenarios and settings. Simulation results reveal the dynamics of the model and show that improving the eye status monitoring device/app factors directly increases the efficiency/Eye-SCOR level. The proposed framework serves as an important step towards understanding and improving the overall performance of the supply chain network of smart eye/vision monitoring systems.
This paper investigates the nonlinear relationship between tourism and economic growth using a balanced sample of 58 countries in three continental samples (Africa, Asia, and Latin America) for the 2003–2017 period. First, we document an asymmetric threshold effect of tourism on economic growth. By utilizing an endogenous threshold regression model, we show that a single tourism threshold cutoff exists and that tourism receipts influence growth only till the threshold cutoff point in all three continental samples; however, this influence is nonexistent past the threshold point. Second, a quantile effect decomposition shows separate marginal effects for the tourism and economic growth relationship across the growth distribution. By using an unconditional quantile regression approach, we show that compared to their regional cohorts, slow- and medium-growth African countries, slow-growth Asian countries, and medium-growth Latin American countries exhibit substantially higher economic growth benefits from tourism. We explain these empirical observations and discuss their policy implications.
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3,499 members
Mengliang Zhang
  • Department of Chemistry
Moses M Prabu
  • Department of Biology
Don Hong
  • Department of Mathematical Sciences
Gregory T Rushton
  • TN STEM Education Center
Norman Weatherby
  • Department of Health and Human Performance
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