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The discrete element method (DEM) is a vital numerical approach for analyzing the mechanical behavior of elastoplastic wet sand. However, parameter uncertainty persists within the mapping between constitutive relationships and inherent model parameters. We propose a Parameter calibration neural network based on Attention, Retention, and improved Transformer for Sequential data (PartsNet), which effectively captures the nonlinear mechanical behavior of wet sand and obtains the optimal parameter combination for the Edinburgh elasto-plastic adhesion constitutive model. Variational autoencoder-based principal component ordering is employed by PartsNet to reduce the high-dimensional dynamic response and extract critical parameters along with their weights. Gated recurrent units are combined with a novel sparse multi-head attention mechanism to process sequential data. The fusion information is delivered by residual multilayer perceptron, achieving the association between sequential response and model parameters. The errors in response data generated by calibrated parameters are quantified by PartsNet based on adaptive differentiation and Taylor expansion. Remarkable calibration capabilities are exhibited by PartsNet across six evaluation indicators, surpassing seven other deep learning approaches in the ablation test. The calibration accuracy of PartsNet reaches 91.29%, and MSE loss converges to 0.000934. The validation experiments and regression analysis confirmed the generalization capability of PartsNet in the calibration of wet sand. The improved sparse attention mechanism optimizes multi-head attention, resulting in a convergence speed of 21.25%. PartsNet contributes to modeling and simulating the precise mechanical properties of complex elastoplastic systems and offers valuable insights for diverse engineering applications. [ABSTRACT FROM AUTHOR]

Currently, the emergence of the endemic Coronavirus disease (COVID‐19) situation still poses a serious threat to public health. However, it remains elusive about the role of fecal microbiota transplantation in treating COVID‐19. We performed a randomized, double‐blind, placebo‐controlled clinical trial enrolling a cohort of 40 COVID‐19 patients with mild‐moderate symptoms. Our results showed that fecal microbiota transplantation provided an amelioration in diarrhoea (p = 0.026) of digestive system and depression (p = 0.006) of neuropsychiatric‐related symptom in COVID‐19 patients, respectively. Meanwhile, we found that the number of patients with diarrhoea decreased from 19 to 0 on day 7 after fecal microbiota transplantation treatment, and it was statistically changed compared to the placebo group (p = 0.047). Of note, the serum concentration of aspartate aminotransferase‐to‐alanine aminotransferase ratio (AST/ALT, fecal microbiota transplantation, pre vs. post: 0.966 vs. 0.817), a biomarker for predicting long COVID‐19, was significantly reduced by fecal microbiota transplantation. In all, our study supports that fecal microbiota transplantation could be a novel therapeutic strategy for COVID‐19 patients with diarrhoea and depressive symptoms, which is potentially valuable in ameliorating long COVID‐19 symptoms. Author Summary: Although public attention towards the SARS‐CoV‐2 is decreasing, the emergence of new variants and the lingering aftermath of long COVID, which includes gastrointestinal and psychiatric symptoms, remains adverse effects on public health. Here, a randomized, double‐blind, placebo‐controlled clinical trial enrolling a cohort of 40 COVID‐19 patients with mild‐moderate symptoms was performed in our study. Our research revealed that fecal microbiota transplantation (FMT) led to alleviation in both diarrhea and depression symptoms among COVID‐19 patients. Additionally, the serum concentration of AST/ALT ratio, a biomarker for predicting long COVID‐19, showed a significant reduction following fecal microbiota transplantation. In conclusion, our research introduces an innovative treatment for COVID‐19 patients experiencing diarrhea and depressive symptoms, while is potentially valuable in ameliorating long COVID‐19 symptoms. [ABSTRACT FROM AUTHOR]

Eu(II) complexes with characteristic d–f transitions demonstrate their potential applications in organic light-emitting diodes (OLEDs) due to their advantages including short excited-state lifetimes, 100% theoretical exciton utilization efficiency and tunable emission colors. Thermally stable and efficient Eu(II) complexes are necessary for blue OLEDs, which are still the bottleneck as compared to red and green OLEDs. A blue-emitting Eu(II) complex can be obtained by appropriate ligand design because the 5d energy levels are sensitive to the external environments. Herein, a series of Eu(II) complexes based on tris(pyrazolyl)borate ligands were designed and synthesized. By carefully adjusting the steric hindrance, a thermally stable sky-blue emission Eu(II) complex bis[hydrotris(3-tert-butylpyrazolyl)borate]europium(II) (Eu–tBu) was obtained by employing the bulky tert-butyl group. The sky-blue OLED based on Eu–tBu was fabricated, showing a maximum external quantum efficiency of 15.7%, a maximum luminance of 52 240 cd m−2, and Commission Internationale del Eclairage coordinates of (0.13, 0.27) at 1000 cd m−2. [ABSTRACT FROM AUTHOR]

The Arrhenius equation is generally adopted for the application of accelerated aging in laboratory tests, and activation energy is one of the important parameters in the equation. In most cases, activation energy was determined by obtaining a series of viscosity data at different temperature, which was a complex process. Therefore, this study aimed to analyze the correlation between activation energy for viscous flow (Eη) and molecular characteristics as well as rheological properties and obtain easily measurable properties that can be used to predict Eη. In this study, nine base asphalt binders were subjected to accelerated aging, and their viscosity, rheological properties, and molecular weight distribution were tested. Small molecule size (SMS) content was the most strongly correlated parameter with Eη for unaged asphalt binder compared with medium molecule size (MMS) content and large molecule size (LMS) content from the result of gray relation analysis. The correlation further weakened after considering aging, which indicated that the change in molecular distribution of asphalt binder may not be the decisive parameter in determining the change in Eη of asphalt binder. Single regression analysis showed a significant correlation between rheological performance parameters and Eη. It was feasible to predict Eη based on the failure temperature and obtain the modified Arrhenius equation. The modified equation could be used to calculate the accelerated aging factor and obtain the aging asphalt and accurate aging status of asphalt binders. By verification, the average error in predicting the Eη of the asphalt binder using the modified equation was less than 5%. [ABSTRACT FROM AUTHOR]

Problem definition: We investigate the effects of waiting time, mainly due to production in a make-to-batch-order (MTBO) system, on consumer choice behavior, pricing, assortment, and model estimation. In an MTBO system, the seller/manufacturer first collects orders placed within a certain period of time into a batch and then starts the production process. After the production of all orders in a batch are complete, the products are then shipped and delivered to individual consumers. Because of batch production and shipping, the disutility of the waiting time exhibits negative externality. Methodology/results: We adopt the widely used multinomial logit (MNL) model as a starting point and incorporate the anticipated wait into consumers' decision making. The derived model, referred to as the MNL with wait model, is a solution of the rational expectation equilibrium and is capable of capturing the effects of negative externality induced by anticipated wait that may change the substitution patterns dramatically. We characterize the multiproduct price optimization problem under the MNL with wait model by establishing a one-to-one mapping between the price vector and the choice probability vector. We find that firms tend to charge higher prices for time-consuming items and charge lower prices for time-saving items compared with the optimal prices under the standard MNL model. In addition to price competition, we also study the Cournot-type competition, in which the decision is the choice probability for each firm and establish the existence of a Nash equilibrium. For assortment optimization, we identify mild conditions under which the optimality of revenue-ordered assortments still holds. However, the assortment problem under the MNL with wait model is generally NP-hard, so we develop approximation algorithms with performance guarantees and provide an easy-to-compute tight upper bound. Moreover, we develop an efficient maximum likelihood-based algorithm for model calibration and further conduct numerical studies to showcase the importance of incorporating disutility due to wait in estimation, pricing, and assortment planning problems. Managerial implications: The MNL with wait model can increase prediction accuracy for consumers' choice behavior especially when they are aware of the potential wait. Failure to take into account the effects of anticipated wait in firms' decision making may lead to substantial losses. Funding: The research of C. Ke is supported in part by the National Natural Science Foundation of China [Grant 72101113]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0346. [ABSTRACT FROM AUTHOR]

The contamination of aquatic environments by heavy metals can have detrimental effects on fish, impacting their growth and overall health, including the regulation of antioxidant genes. An investigation was carried out to assess the distribution and habitat of Gymnocypris eckloni in the Yellow River basin. Simultaneously, heavy metal concentrations in its habitat and in selected locations within the upper Yellow River were measured. In an effort to explore the potential roles of specific genes in antioxidant responses, G. eckloni was exposed to low concentrations of copper (Cu2+) and lead (Pb2+) for varying durations (12, 24, and 48 hours). The mRNA levels of GeCu/Zn-SOD, GeMn-SOD, GeHsp90, and GeMT were quantified in the gills, kidneys, and liver through qRT-PCR. The findings suggest that the habitat of G. eckloni is generally safe; however, occasional exceedances of safety standards could pose a potential threat to its growth. Importantly, the expression of GeCu/Zn-SOD, GeMn-SOD, GeHsp90, and GeMT exhibited responses to the low concentrations of copper-induced and lead-induced stress. Notably, GeCu/Zn-SOD, GeMn-SOD, and GeMT demonstrated heightened sensitivity to lead compared to copper. Furthermore, the expression of these genes displayed tissue-specific responses under identical metal stress conditions. These results indicate that GeCu/Zn-SOD, GeMn-SOD, GeHsp90, and GeMT genes have the potential to serve as early, sensitive biomarkers for the detection of metal toxicity induced by Cu2+ and Pb2+. This study also provides valuable insights into the functioning of antioxidant genes under oxidative stress in fish. [ABSTRACT FROM AUTHOR]

The study of asphalt mixture in unfavourable climate conditions has been receiving an increasing attention in pavement engineering. This study aimed to investigate the moisture resistance property of stone matrix asphalt (SMA) in continuous rainfall and high-temperature conditions. Meanwhile, this study employed five indicators to evaluate the moisture resistance property of SMA and analysed the correlation between them. Specifically, rutting depth, creep slope (CS), slipping number (LCSN), tensile strength ratio (TSR) and adhesion failure were used for quantitative evaluation. Climate data of the most unfavourable condition in Shanghai in the past 20 years were collected and input in an accelerated weathering test device for indoor simulation. Hamburg wheel tracking test, indirect tensile test and water boiling test were employed to characterise the moisture resistance property of SMA. As a result, it revealed that moisture resistance of SMA could drop to a minimum value after being exposed to the environment for more than 5 days, and the moisture resistance of SMA could rebound at around 10 days of treatment. Through correlation analysis, it was found that for SMA, TSR presented a good linear correlation with the reciprocal of CS (1/CS) and LCSN showed a good correlation with 1/CS and rutting depth. [ABSTRACT FROM AUTHOR]

The most important problems encountered in the design and operation of oil pipelines in permafrost regions are frost heaving and thawing settlement. In this paper, we conducted an investigation and analysis on the status of freezing damage of the operating oil pipelines. Based on the investigation and research, we calculated the temperature field of the oil pipeline and analysed the thawing ability under the conditions of the strain criterion. Finally, we proposed a design method for pipeline insulation and the maximum amount of thawing displacement that the pipeline can withstand. It has certain guiding significance for the thermal insulation design of oil pipelines in permafrost regions.