Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems

In the paper, we propose a novel approach for solving Bayesian inverse problems with physics-informed invertible neural networks (PI-INN). The architecture of PI-INN consists of two sub-networks: an invertible neural network (INN) and a neural basis network (NB-Net). The invertible map between the parametric input and the INN output with the aid of NB-Net is constructed to provide a tractable estimation of the posterior distribution, which enables efficient sampling and accurate density evaluation. Furthermore, the loss function of PI-INN includes two components: a residual-based physics-informed loss term and a new independence loss term. The presented independence loss term can Gaussianize the random latent variables and ensure statistical independence between two parts of INN output by effectively utilizing the estimated density function. Several numerical experiments are presented to demonstrate the efficiency and accuracy of the proposed PI-INN, including inverse kinematics, inverse problems of the 1-d and 2-d diffusion equations, and seismic traveltime tomography

Effect of Personalized Incentives on Dietary Quality of Groceries Purchased A Randomized Crossover Trial

Importance Many factors are associated with food choice. Personalized interventions could help improve dietary intake by using individual purchasing preferences to promote healthier grocery purchases. Objective To test whether a healthy food incentive intervention using an algorithm incorporating customer preferences, purchase history, and baseline diet quality improves grocery purchase dietary quality and spending on healthy foods. Design, Setting, and Participants This was a 9-month randomized clinical crossover trial (AB–BA) with a 2- to 4-week washout period between 3-month intervention periods. Participants included 224 loyalty program members at an independent Rhode Island supermarket who completed baseline questionnaires and were randomized from July to September 2018 to group 1 (AB) or group 2 (BA). Data analysis was performed from September 2019 to May 2020. Intervention Participants received personalized weekly coupons with nutrition education during the intervention period (A) and occasional generic coupons with nutrition education during the control period (B). An automated study algorithm used customer data to allocate personalized healthy food incentives to participant loyalty cards. All participants received a 5% grocery discount. Main Outcomes and Measures Grocery Purchase Quality Index–2016 (GPQI-16) scores (range, 0-75, with higher scores denoting healthier purchases) and percentage spending on targeted foods were calculated from cumulative purchasing data. Participants in the top and bottom 1% of spending were excluded. Paired t tests examined between-group differences. Results The analytical sample included 209 participants (104 in group 1 and 105 in group 2), with a mean (SD) age of 55.4 (14.0) years. They were predominantly non-Hispanic White (193 of 206 participants [94.1%]) and female (187 of 207 participants [90.3%]). Of 161 participants with income data, 81 (50.3%) had annual household incomes greater than or equal to $100 000. Paired t tests showed that the intervention increased GPQI-16 scores (between-group difference, 1.06; 95% CI, 0.27-1.86; P = .01) and percentage spending on targeted foods (between-group difference, 1.38%; 95% CI, 0.08%-2.69%; P = .04). During the initial intervention period, group 1 (AB) and group 2 (BA) had similar mean (SD) GPQI-16 scores (41.2 [6.6] vs 41.0 [7.5]) and mean (SD) percentage spending on targeted healthy foods (32.0% [10.8%] vs 31.0% [10.5%]). During the crossover intervention period, group 2 had a higher mean (SD) GPQI-16 score than group 1 (42.9 [7.7] vs 41.0 [6.8]) and mean (SD) percentage spending on targeted foods (34.0% [12.1%] vs 32.0% [13.1%]). Conclusions and Relevance This pilot trial demonstrated preliminary evidence for the effectiveness of a novel personalized healthy food incentive algorithm to improve grocery purchase dietary quality. Trial Registration ClinicalTrials.gov Identifier: NCT0374805

Effect of Vegetation on the Flow of a Partially-Vegetated Channel

Abstract A vegetated channel commonly exists in the natural environment. Over recent decades, many researchers have taken an interest in this field. The hydraulic characteristics of flow over vegetated channels are complex. Vegetation significantly affects the flow resistance and turbulence, resulting in sediments, nutrients, and contaminants transportation. Thus, understanding the impact of vegetation on flow structures is important for river and environment management. However, most attention on vegetated channel flow focuses on single-layered vegetated channels. There are few studies on the impact of double-layered, partially placed vegetation on open channel flow. To fill this research gap, this paper aims to investigate the impact of vegetation on the flow velocity of a double-layered, partially placed vegetated channel.</jats:p

A nonlinear analytical model of composite plate structure with an MRE function layer considering internal magnetic and temperature fields

To better exert the vibration suppression effect of magnetorheological elastomer (MRE) embedded into a composite structure with structural and functional integration advantage, this study proposes a nonlinear analytical model of such composite plate with an MRE function (MREF) layer, accounting for internal magnetic and temperature fields for the first time. Initially, a 9-layer fiber metal laminated (FML) plate with the MREF composites, consisting of two layers of metal protective skins, two layers of fiber-reinforced polymer (FRP) and one layer of MREF, is taken as an example to describe such a modelling method. Nonlinear expressions of elastic moduli of MRE and FRP involving thermal and magnetic fitting coefficients are also proposed, followed by derivation of the energy expressions of the constituent layers by the Rayleigh-Ritz method. After the free and forced vibrations are solved, the identification procedure of fitting coefficients is described and some literature results are employed to preliminarily validate this model without consideration of internal magnetic field or temperature field or both. Finally, dynamic experiments under different magnetic and temperature conditions are undertaken. The detailed comparison of the natural frequencies and resonant responses are conducted to provide a solid validation of the model developed. It has been found that enlarging the magnetic and temperature fields both facilitate the improvement of the anti-vibration performance. Also, by further increasing the shear modulus of MRE, the volume fraction of carbonyl iron particles or the thickness ratio of the MRE layer to the overall structure, a better vibration suppression capability can be obtained

Appearance and performance enhancing drug usage and body image across three age cohorts of fitness enthusiast men

Background: Because research findings on men\u27s body image and compensatory behaviors (e.g., appearance and performance enhancing drug (APED) usage) across the lifecycle in men is contradictory, the purpose of this study was to examine the relationship between age and body image concerns, and to investigate the specific types of APEDs used across the three age cohorts (emerging, established, and middle-aged men). Methods: Using an online survey and a sample of 1020 fitness enthusiast men, we investigated (1) type of APED/supplements used across three periods of life (emerging adulthood, established adulthood, middle age), and (2) any relationship between period in life and body image. Results: emerging adult participants had significantly lower appreciation for body functionality compared to their older counterparts. Established adult men had higher drive for leanness and muscle dysmorphia symptoms compared to the other two groups. The data suggest that men vary in severity of body image dissatisfaction depending on their age. All participants in the current study participated in polypharmacy of APED usage, and the associations between age cohorts and APED consumption were significant. Established adult men endorsed a more muscular body, higher drive for leanness, and they proportionally consume more APEDs compared to their younger (and older) counterparts. Conclusions: At this stage of life, men may start noticing age-related changes to their body, which may lead to preoccupations with their health and functionality, and they may use products to help counteract a declining metabolism and overall shift in physical health. Findings of this study could be beneficial for familiarizing clinicians with focused knowledge of intergenerational dynamics, which allows for better understanding of the diversity of challenges and opportunities facing each age cohort with respect to the aging body

Targeted retail coupons influence category-level food purchases over 2-years

Abstract Background Targeted coupons strongly influence purchasing behavior and may represent an innovative approach for improving dietary behaviors. Methods The retail analytics firm, Dunnhumby, provided secondary retail data containing grocery transactions, targeted coupon exposures, and coupon use for 2500 households over 2-years. The USDA Quarterly At-Home Food Purchasing Database was used to categorize individual foods into 52 categories and combined into 12 food groups. Mixed effects linear models estimated the difference-in-difference effects of coupon exposure on category-level purchase rate/wk. pre- and post-campaign; models also tested effect modification by food category. Results Category-level food purchases significantly increased post-campaign. Mean (SD) food purchases/wk. Among exposed households (17.34 (13.08) units/wk) vs. unexposed households (3.75 (4.59) units/wk) were higher (p < 0.001). Difference-in-difference effects of coupon exposure showed a higher increase in purchase rate among exposed vs. unexposed households (5.73 vs. 0.67, p < 0.001). Food category significantly modified the association between coupon exposure and coupon campaign. Category-level purchase rate among exposed vs. unexposed households was relatively higher in less healthful (e.g. convenience foods) vs. more healthful categories (e.g. nuts) with a 1.17 unit/wk. increase in convenience foods purchase (p < 0.001) vs. a 0.03 unit/wk. increase in nuts (p < 0.001). Exploratory analyses suggested that price elasticity of food categories for targeted coupons (1.02–2.81) was higher than previous estimates for untargeted coupons. Conclusion Across food categories, coupon exposure increased category-level purchase rate, with a relatively larger effect size for less healthful than more healthful categories. Promising results from this preliminary study suggest that experimental research is warranted to determine whether targeting with the explicit purpose of improving dietary quality can more effectively influence diet, and whether it can do so more cost effectively

MHDnet: Multi-modes multiscale physics informed neural networks for solving magnetohydrodynamics problems

Modeling and control of the magnetohydrodynamics (MHD) system remain a challenging problem, which involves the coupling between fluid dynamics and electromagnetism with the nonlinear, multiscale spatiotemporal features. To address these issues, we develop the MHDnet as a physics-informed learning approach to MHD problems with the multi-modes multiscale feature embedding into multiscale neural network architecture, which can accelerate the convergence of the neural networks (NN) by alleviating the interaction of magnetic fluid coupling across different frequency modes. Three different mathematical formulations are considered and named the original formulation ($B$), magnetic vector potential formulation ($A_1$), and divergence-free both magnetic induction and velocity formulation ($A_2$). The residual of them, together with the initial and boundary conditions, are emerged into the loss function of MHDnet. Moreover, the pressure fields of three formulations, as the hidden state, can be obtained without extra data and computational cost. Several numerical experiments are presented to demonstrate the performance of the proposed MHDnet compared with different NN architectures and numerical formulations, and the pressure fields can also be given by MHDnet with $A_1$ and $A_2$ formulations with high accuracy

A deep reinforcement learning with dynamic spatio-temporal graph model for solving urban logistics delivery planning problems

The urban logistics delivery planning problems are a crucial component of urban spatial decision analysis. Most studies typically focus on traditional urban logistics delivery planning problems and ignore real-time traffic information. With the advancement of urbanization, real-time traffic networks play a critical role. However, previous studies have utilized heuristic methods to solve urban logistics delivery planning with real-time traffic information problems, and few studies have applied deep reinforcement learning methods to tackle this problem. Deep reinforcement learning methods solving traditional logistics delivery planning problems overlook the impact of dynamic spatio-temporal features on route planning. In this study, we propose a new deep reinforcement method called DRLDSTG. The method introduces the dynamic spatio-temporal graph model into a deep reinforcement learning method to capture these dynamic features from urban logistics delivery planning tasks. The actor-critic with maximum entropy method is employed to train the model and determine the optimal policy function. The experimental results indicated that the proposed method can achieve a superior solution with faster computational efficiency compared to commercial software and heuristic methods. Compared to other deep reinforcement learning methods, our method can more effectively learn dynamic spatio-temporal features from environments, demonstrating promising applications in cities