Equestrian Trail Riding: An Emerging Economic Contributor To The Local Rural Appalachian Economy

The purpose of this paper is three-fold. First to summarize the importance of tourism in the Appalachian region with a focus on the State of Kentucky; second, to consider adventure tourism and the equestrian and trail riding segment as a potential contributor to Kentucky adventurism tourism; and third, to illustrate the economic value of trail riding in the form of an economic impact study which indicates levels of community economic development opportunity. Importantly, for this publication, is the fact that this research was conducted by undergraduate students at Berea College. This initial research was conducted by undergraduate within the Entrepreneurship for the Public Good Program at Berea College in the summer of 2008 and the economic impact study field work was conducted by freshman undergraduates in a Creative Writing class with a focus on adventure tourism in the fall of 2008

Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments

Firms implementing digital advertising campaigns face a complex problem in determining the right match between their advertising creatives and target audiences. Typical solutions to the problem have leveraged non-experimental methods, or used "split-testing" strategies that have not explicitly addressed the complexities induced by targeted audiences that can potentially overlap with one another. This paper presents an adaptive algorithm that addresses the problem via online experimentation. The algorithm is set up as a contextual bandit and addresses the overlap issue by partitioning the target audiences into disjoint, non-overlapping sub-populations. It learns an optimal creative display policy in the disjoint space, while assessing in parallel which creative has the best match in the space of possibly overlapping target audiences. Experiments show that the proposed method is more efficient compared to naive "split-testing" or non-adaptive "A/B/n" testing based methods. We also describe a testing product we built that uses the algorithm. The product is currently deployed on the advertising platform of JD.com, an eCommerce company and a publisher of digital ads in China

Parallel Experimentation in a Competitive Advertising Marketplace

When multiple firms are simultaneously running experiments on a platform, the treatment effects for one firm may depend on the experimentation policies of others. This paper presents a set of causal estimands that are relevant to such an environment. We also present an experimental design that is suitable for facilitating experimentation across multiple competitors in such an environment. Together, these can be used by a platform to run experiments "as a service," on behalf of its participating firms. We show that the causal estimands we develop are identified nonparametrically by the variation induced by the design, and present two scalable estimators that help measure them in typical high-dimensional situations. We implement the design on the advertising platform of JD.com, an eCommerce company, which is also a publisher of digital ads in China. We discuss how the design is engineered within the platform's auction-driven ad-allocation system, which is typical of modern, digital advertising marketplaces. Finally, we present results from a parallel experiment involving 16 advertisers and millions of JD.com users. These results showcase the importance of accommodating a role for interactions across experimenters and demonstrates the viability of the framework

Application of the energy finite element analysis to vibration of beams with stepped thickness and variable cross-section

Energy finite element analysis (EFEA) has been developed to compute the energy distribution of vibrating structures. The method adopts the energy density as the basic variable of differential equation. The energy density can be used to analyze the behavior of vibrating beams. Firstly, an EFEA equation is obtained from the classical displacement equation. In the applications of uniform and non-uniform beams, the EFEA results are compared with the analytical and FEA results. Secondly, a junction formulation solving the discontinuity problem of energy density at the junction of two beams with stepped thickness is proposed. The EFEA equation combined with junction formulation is used to solve the energy transmission problem of the coupling beams with stepped thickness and variable cross-section. The smoothed results of coupling beams are achieved, and the differences of energy density at the junctions are analyzed. The feasibility of the EFEA approach is validated by using several design examples under the various frequencies and structural damping loss factors

Comparison Lift: Bandit-based Experimentation System for Online Advertising

Comparison Lift is an experimentation-as-a-service (EaaS) application for testing online advertising audiences and creatives at JD.com. Unlike many other EaaS tools that focus primarily on fixed sample A/B testing, Comparison Lift deploys a custom bandit-based experimentation algorithm. The advantages of the bandit-based approach are two-fold. First, it aligns the randomization induced in the test with the advertiser's goals from testing. Second, by adapting experimental design to information acquired during the test, it reduces substantially the cost of experimentation to the advertiser. Since launch in May 2019, Comparison Lift has been utilized in over 1,500 experiments. We estimate that utilization of the product has helped increase click-through rates of participating advertising campaigns by 46% on average. We estimate that the adaptive design in the product has generated 27% more clicks on average during testing compared to a fixed sample A/B design. Both suggest significant value generation and cost savings to advertisers from the product

Single and composite damage mechanisms of soil polyethylene/polyvinyl chloride microplastics to the photosynthetic performance of soybean (Glycine max [L.] merr.)

IntroductionAdverse impacts of soil microplastics (MPs, diameter<5 mm) on vegetative growth and crop production have been widely reported, however, the single and composite damage mechanisms of polyethylene (PE) /polyvinyl chloride (PVC) microplastics (MPs) induced photosynthesis inhibition are still rarely known.MethodsIn this study, two widely distributed MPs, PE and PVC, were added to soils at a dose of 7% (dry soil) to examine the single and composite effects of PE-MPs and PVC-MPs on the photosynthetic performance of soybean.ResultsResults showed PE-MPs, PVC-MPs and the combination of these two contaminants increased malondialdehyde (MDA) content by 21.8-97.9%, while decreased net photosynthesis rate (Pn) by 11.5-22.4% compared to those in non-stressed plants, PVC MPs caused the most severe oxidative stress, while MPs stress resulted in Pn reduction caused by non-stomatal restriction. The reason for this is the single and composite MPs stress resulted in a 6% to 23% reduction in soybean PSII activity RCs reaction centers, along with negative effects on soybean PSII energy uptake, capture, transport, and dissipation. The presence of K-band and L-band also represents an imbalance in the number of electrons on the donor and acceptor side of PSII and a decrease in PSII energy transfer. Similarly, PVC single stress caused greater effects on soybean chloroplast PSII than PE single stress and combined stresses.DiscussionPE and PVC microplastic stress led to oxidative stress in soybean, which affected the structure and function of photosynthetic PSII in soybean, ultimately leading to a decrease in net photosynthetic rate in soybean

Disaggregate Network Effects on Two-Sided Platforms

This dissertation empirically explores the mechanisms through which software variety affects hardware purchases in markets with a hardware-software structure. Software variety may provide value to consumers because (i) it allows them to find a product better matched to their preferences (heterogeneity), and (ii) it may satisfy their need for different products across consumption occasions (within-person demand for variety). Using household-level coffee purchases and Keurig machine adoption data, I first build and estimates a consumer demand model for coffee allowing both within-consumer demand for variety and cross-consumer heterogeneity. Repeated purchases and trip-level variations in product prices and availabilities identify the household preferences, which allows for more reliable estimates. With the preference estimates, I then calculate the value of consuming Keurig's coffee pods (K-Cups) for each household and week, which I call the option value of K-Cups in the paper. Finally, I directly link the option values to their Keurig machine adoption decisions via a dynamic discrete choice model. Estimation results indicate variety in K-Cups, particularly brand variety, increases the option values for households, and the higher option values, in turn, increases machine adoption. To understand the mechanisms through which K-Cup variety influence Keurig machine adoption, I simulate the counterfactual adoption rates if households (a) can only choose their favorite K-Cup brands or ground coffee, and (b) have a library of K-Cups to choose from but not their favorite brands. Simulation results show that adoption rate would be about 2/3 of the current level in the later case compared to 1/4 in the former scenario, and thus indicate demand for variety is the primary mechanism under which K-Cup variety is valuable to consumers. Moreover, I show without third-party brands, (i) Keurig adoptions would have been much lower at the end of 2013; and (ii) the median consumer welfare loss conditional on the adoption of Keurig machines is large compared to their spending on coffee with substantial heterogeneity. Furthermore, Keurig-owned K-Cups revenue would have been more than 40% without third-party brands because of lower adoption base