What triggers sharing in viral marketing? The role of emotion and social feature

Viral marketing has attracted attention from both academics and practitioners. With the rise of user-generated content (UGC) and broadcasting networks, viral online video advertising campaigns (viral advertising in short) are an emerging trend in viral marketing. Previous literature mainly studied the influence of network structure on viral advertising. Here, we extend such works by decomposing the diffusion network into individual sharing behavior. We based our work on theories of emotion and social networks by proposing a framework that specifies the role of emotion and social feature on individuals’ sharing of online video advertisements in viral marketing campaigns. The framework will be tested using real-world data extracted from online broadcasting networks in the future work

turbo-RANS: Straightforward and Efficient Bayesian Optimization of Turbulence Model Coefficients

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. Although tuning these coefficients can produce significantly improved predictive accuracy, their default values are often used. We believe users do not calibrate RANS models for several reasons: there is no clearly recommended framework to optimize these coefficients; the average user does not have the expertise to implement such a framework; and, the optimization of the values of these coefficients can be a computationally expensive process. In this work, we address these issues by proposing a semi-automated calibration of these coefficients using a new framework based on Bayesian optimization. We introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse, or dense reference data. We demonstrate the computationally efficient performance of turbo-RANS for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel. In the first two examples, we calibrate the $k$-$\omega$ shear stress transport (SST) turbulence model and, in the last example, we calibrate user-specified coefficients for the Generalized $k$-$\omega$ (GEKO) model in Ansys Fluent. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure. Towards the goal of facilitating RANS turbulence closure model calibration, we provide an open-source implementation of the turbo-RANS framework that includes OpenFOAM, Ansys Fluent, and solver-agnostic templates for user application.Comment: 30 pages, 18 figures. Submitted to Journal of Computational Physic

Identification and care of patients at risk of post-stroke dementia

PhD ThesisStroke can directly cause cognitive difficulties but also increases the risk of future dementia. There is often less focus on these consequences during standard care, which tends to concentrate on physical function. The seven publications described in this thesis focussed on four aims, which were to: a) describe the impact of cognitive difficulties post-stroke over time b) understand patient and professional views regarding current care for stroke-survivors with memory problems c) describe the acceptability and accuracy of dementia risk prediction models following stroke d) understand healthcare professional views about how to meet the cognitive needs of stroke-survivors. A mixed-methods approach was used to address these aims including: a) A systematic review of studies found there was a tendency towards cognitive decline, but this was not consistent as patients post-stroke can stabilise or even recover; b) Semi-structured interviews with i) stroke-survivors reporting memory difficulties and their family carers and ii) primary and secondary care professionals consistently reported clear gaps in care for stroke survivors with memory deficits; c) Harmonisation of international stroke cohorts to externally validate existing dementia risk prediction models which have not validated well in stroke populations. Further, in the qualitative interviews, patients, family carers and healthcare professionals identified challenges to their implementation; d) A national electronic-Delphi survey found that stroke clinicians believe assessment of post-stroke cognition needs better integration into services with clarification of when and where this should be done to streamline access. The gaps in current services mean that the support available to care for and identify those at greatest risk for dementia is lacking. Patients and carers should be offered information about the long-term cognitive consequences poststroke. If required, they should be encouraged to seek assistance in the community with the aim of being directly referred back into specialist services for assessment and intervention.NIH

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands

Predicting online product sales via online reviews, sentiments, and promotion strategies

Purpose – The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach – The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings – This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume. Originality/value – This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies

SPECIFICATIONS OF A PROTOTYPE SOFTWARE SYSTEM FOR DEVELOPING VARIABLE-RATE TREATMENT PRESCRIPTIONS FOR USE IN PRECISION AGRICULTURE

This paper discusses the process of developing variable-rate treatment prescriptions and gives specifications for a prototype software system for implementing that process. The process is based on statistical analysis of data from embedded field trials, and incorporates producer preferences in determining a treatment prescription. The system can be used by researchers in agricultural research stations for developing prescriptions for commercial agricultural producers. The specifications provided are general enough to be implemented using a variety of statistical and database packages that are available to researchers. In addition to these specifications we provide online access to source code for implementing the system in SAS. We use this system to develop treatment prescriptions for a commercial cotton farming operation in northeast Louisiana. The prescriptions are based on data from a precision agriculture experiment conducted in 2006. The objective of that study was to compare the effects of five nitrogen rates on cotton lint yield across several soil types for the purpose of developing a variable-rate nitrogen treatment prescription for future use on that farm. Several possible producer preferences were incorporated with the results of the field trial to produce optional treatment prescriptions for the producer

Phenyl radical + propene: a prototypical reaction surface for aromatic-catalyzed 1,2-hydrogen-migration and subsequent resonance-stabilized radical formation

The C[subscript 9]H[subscript 11] potential energy surface (PES) was experimentally and theoretically explored because it is a relatively simple, prototypical alkylaromatic radical system. Although the C[subscript 9]H[subscript 11] PES has already been extensively studied both experimentally (under single-collision and thermal conditions) and theoretically, new insights were made in this work by taking a new experimental approach: flash photolysis combined with time-resolved molecular beam mass spectrometry (MBMS) and visible laser absorbance. The C[subscript 9]H[subscript 11] PES was experimentally accessed by photolytic generation of the phenyl radical and subsequent reaction with excess propene (C[subscript 6]H[subscript 5] + C[subscript 3]H[subscript 6]). The overall kinetics of C[subscript 6]H[subscript 5] + C[subscript 3]H[subscript 6] was measured using laser absorbance with high time-resolution from 300 to 700 K and was found to be in agreement with earlier measurements over a lower temperature range. Five major product channels of C[subscript 6]H[subscript 5] + C[subscript 3]H[subscript 6] were observed with MBMS at 600 and 700 K, four of which were expected: hydrogen (H)-abstraction (measured by the stable benzene, C[subscript 6]H[subscript 6], product), methyl radical (CH[subscript 3])-loss (styrene detected), H-loss (phenylpropene isomers detected) and radical adduct stabilization. The fifth, unexpected product observed was the benzyl radical, which was rationalized by the inclusion of a previously unreported pathway on the C[subscript 9]H[subscript 11] PES: aromatic-catalysed 1,2-H-migration and subsequent resonance stabilized radical (RSR, benzyl radical in this case) formation. The current theoretical understanding of the C[subscript 9]H[subscript 11] PES was supported (including the aromatic-catalyzed pathway) by quantitative comparisons between modelled and experimental MBMS results. At 700 K, the branching to styrene + CH[subscript 3] was 2-4 times greater than that of any other product channel, while benzyl radical + C[subscript 2]H[subscript 4] from the aromatic-catalyzed pathway accounted for ∼10% of the branching. Single-collision conditions were also simulated on the updated PES to explain why previous crossed molecular beam experiments did not see evidence of the aromatic-catalyzed pathway. This experimentally validated knowledge of the C[subscript 9]H[subscript 11] PES was added to the database of the open-source Reaction Mechanism Generator (RMG), which was then used to generalize the findings on the C[subscript 9]H[subscript 11] PES to a slightly more complicated alkylaromatic system.Think Global Education Trus

Numerical modelling of interaction between aluminium structure and explosion in soil

In this paper, a graphics processing unit-accelerated smoothed particle hydrodynamics solver is presented to simulate the three-dimensional explosions in soils and their damage to aluminium structures. To achieve this objective, a number of equations of state and constitutive models required to close the governing equations are incorporated into the proposed smoothed particle hydrodynamics framework, including the Jones-Wilkins-Lee equation of state for explosive materials, the Grüneisen equation of state for metals, the elastic-perfectly plastic constitutive model for metals, and the elastoplastic and elasto-viscoplastic constitutive models for soils. The proposed smoothed particle hydrodynamics methodology was implemented using the Compute Unified Device Architecture programming interface on an NVIDIA graphics processing unit in order to improve the computational efficiency. The various components of the proposed methodology were validated using four test cases, namely, a C4 detonation and an aluminium bar expanded by denotation to validate the modelling of explosion, a cylindrical Taylor bar impact test case to validate the modelling of large deformation in metals, a sand collapse test for the modelling of soils. Following the validation, the proposed method was used to simulate the detonation of an explosive material (C4) in soil and the concomitant deformation of an aluminium plate resulting from this explosion. The predicted results of this simulation are shown to be in good conformance with available experimental data. Finally, it is shown that the proposed graphics processing unit-accelerated SPH solver is able to model interaction problems involving millions of particles in a reasonable time. © 2021 The Author

Organic cation transporter 1 (OCT1) modulates multiple cardiometabolic traits through effects on hepatic thiamine content.

A constellation of metabolic disorders, including obesity, dysregulated lipids, and elevations in blood glucose levels, has been associated with cardiovascular disease and diabetes. Analysis of data from recently published genome-wide association studies (GWAS) demonstrated that reduced-function polymorphisms in the organic cation transporter, OCT1 (SLC22A1), are significantly associated with higher total cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride (TG) levels and an increased risk for type 2 diabetes mellitus, yet the mechanism linking OCT1 to these metabolic traits remains puzzling. Here, we show that OCT1, widely characterized as a drug transporter, plays a key role in modulating hepatic glucose and lipid metabolism, potentially by mediating thiamine (vitamin B1) uptake and hence its levels in the liver. Deletion of Oct1 in mice resulted in reduced activity of thiamine-dependent enzymes, including pyruvate dehydrogenase (PDH), which disrupted the hepatic glucose-fatty acid cycle and shifted the source of energy production from glucose to fatty acids, leading to a reduction in glucose utilization, increased gluconeogenesis, and altered lipid metabolism. In turn, these effects resulted in increased total body adiposity and systemic levels of glucose and lipids. Importantly, wild-type mice on thiamine deficient diets (TDs) exhibited impaired glucose metabolism that phenocopied Oct1 deficient mice. Collectively, our study reveals a critical role of hepatic thiamine deficiency through OCT1 deficiency in promoting the metabolic inflexibility that leads to the pathogenesis of cardiometabolic disease