A Benchmark Dataset for Understandable Medical Language Translation

In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions. The dataset contains 12,801 training samples, 1,015 validation samples, and 1,016 testing samples. We then evaluate one naive and six deep learning-based approaches on the MedLane dataset, including directly copying, a statistical machine translation approach Moses, four neural machine translation approaches (i.e., the proposed PMBERT-MT model, Seq2Seq and its two variants), and a modified text summarization model PointerNet. To compare the results, we utilize eleven metrics, including three new measures specifically designed for this task. Finally, we discuss the limitations of MedLane and baselines, and point out possible research directions for this task

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC

Bayesian variable selection and estimation in binary quantile regression using global-local shrinkage priors

In this paper, we construct a Bayesian hierarchical model with global-local shrinkage priors for the regression coefficients, which includes the horseshoe prior and normal-gamma prior. This model is used for high-dimensional quantile regression models with dichotomous response data. We have developed an efficient sampling algorithm to generate posterior samplings for making posterior inference. We use a location-scale mixture representation of the asymmetric Laplace distribution. We assess the performance of the proposed methods through Monte Carlo simulations and two real-data applications in terms of parameter estimation and variable selection. Numerical results demonstrate that the proposed methods perform comparably with existing Bayesian methods under a variety of scenarios

Morphology controlled performance of ternary layered oxide cathodes

Abstract With the rapid advancement of electric vehicle technologies, ternary layered oxide cathodes in commercial Li-ion batteries have become increasingly promising due to their high energy density and low cost. However, the need for higher energy density and cell stability has posed significant challenges in their development. While various coating and doping strategies have been demonstrated to improve the rate and cycle performance of cathode materials, morphology-focused modifications of these cathodes are sometimes overlooked, despite their impact on electrochemical performance. Herein, this review focuses on the morphological relationship of cathode materials to their electrochemical performance. We summarize the effects of cathode materials morphology on Li-ion diffusion and stability. We also discuss the recent advances in the development of cathode materials with different morphologies. Finally, we present future perspectives for the design of cathode materials with optimized morphologies to promote their commercialization and fundamental research

Sparse Bayesian variable selection in high‐dimensional logistic regression models with correlated priors

In this paper, we propose a sparse Bayesian procedure with global and local(GL) shrinkage priors for the problems of variable selection and classification in high-dimensional logistic regression models. In particular, we consider two types of GL shrinkage priors for the regression coefficients, the horseshoe (HS)prior and the normal-gamma (NG) prior, and then specify a correlated prior for the binary vector to distinguish models with the same size. The GL priors are then combined with mixture representations of logistic distribution to construct a hierarchical Bayes model that allows efficient implementation of a Markov chain Monte Carlo (MCMC) to generate samples from posterior distribution. We carry out simulations to compare the finite sample performances of the proposed Bayesian method with the existing Bayesian methods in terms of the accuracy of variable selection and prediction. Finally, two real-data applications are provided for illustrative purposes

Digital Twin Smart Water Conservancy: Status, Challenges, and Prospects

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field

Prediction of Anthracnose Risk in Large-Leaf Tea Trees Based on the Atmospheric Environmental Changes in Yunnan Tea Gardens—Cox Regression Model and Machine Learning Model

Crop diseases pose a major threat to agricultural production, quality, and sustainable development, highlighting the importance of early disease risk prediction for effective disease control. Tea anthracnose can easily occur in Yunnan under high-temperature and high-humidity environments, which seriously affects the ecosystem of tea gardens. Therefore, the establishment of accurate, non-destructive, and rapid prediction models has a positive impact on the conservation of biodiversity in tea plantations. Because of the linear relationship between disease occurrence and environmental conditions, the growing environmental conditions can be effectively used to predict crop diseases. Based on the climate data collected by Internet of Things devices, this study uses LASSO-COX-NOMOGRAM to analyze the expression of tea anthracrum to different degrees through Limma difference analysis, and it combines Cox single-factor analysis to study the influence mechanism of climate and environmental change on tea anthracrum. Modeling factors were screened by LASSO regression, 10-fold cross-validation and Cox multi-factor analysis were used to establish the basis of the model, the nomogram prediction model was constructed, and a Shiny- and DynNOM-visualized prediction system was built. The experimental results showed that the AUC values of the model were 0.745 and 0.731 in the training set and 0.75 and 0.747 in the verification set, respectively, when the predicted change in tea anthracnose disease risk was greater than 30% and 60%, and the calibration curve was in good agreement with the ideal curve. The accuracy of external verification was 83.3% for predicting tea anthracnose of different degrees. At the same time, compared with the traditional prediction method, the method is not affected by the difference in leaf background, which provides research potential for early prevention and phenotypic analysis, and also provides an effective means for tea disease identification and harm analysis

Geographical distribution of trace elements (selenium, zinc, iron, copper) and case fatality rate of COVID-19: A national analysis across conterminous USA

Severe outcome particularly death is the largest burden of COVID-19. Clinical observations showed preliminary data that deficiency in certain trace elements, essential for the normal activity of immune system, may be associated with worse COVID-19 outcome. Relevant study of environmental epidemiology has yet to be explored. We investigated the geographical association between concentrations of Se, Zn, Fe and Cu in surface soils and case fatality rate of COVID-19 in USA. Two sets of database, including epidemiological data of COVID-19 (including case fatality rate, from the University of John Hopkinson) and geochemical concentration data of Se, Zn, Fe and Cu in surface soils (from the National Geochemical Survey), were mapped according to geographical location at the county level across conterminous USA. Characteristics of population, socio-demographics and residential environment by county were also collected. Seven cross-sectional sampling dates, with a 4-week interval between adjacent dates, constructed an observational investigation over 24 weeks from October 8, 2020, to March 25, 2021. Multivariable fractional (logit) outcome regression analyses were used to assess the association with adjustment for potential confounding factors. In USA counties with the lowest concentration of Zn, the case fatality rate of COVID-19 was the highest, after adjustment for other influencing factors. Associations of Se, Fe and Cu with case fatality rate of COVID-19 were either inconsistent over time or disappeared after adjustment for Zn. Our large study provides epidemiological evidence suggesting an association of Zn with COVID-19 severity, suggesting Zn deficiency should be avoided

The effect of impurity on miscible CO2 displacement mechanism

International audienceThe CO2 displacement is one of the gasflooding Enhanced Oil Recovery (EOR) methods. The application from volatile oil to black oil is popular mainly because CO2 requires a relatively low miscibility pressure, which is suitable to most reservoir conditions. However, CO2 always contains some impurity, such as CH4, H2S and N2, leading to the change of phase behavior and flooding efficiency. Whether the gasflooding achieves successfully miscible displacement depends on the reservoir pressure and temperature, injected solvent and crude oil compositions. So three different types of oil samples from the real field are selected and mixtures of CH4, H2S and N2 with various CO2 concentrations as the solvent are considered. After a series of experimental data are excellently matched, three nine-pseudocomponent models are generated based on the thermodynamic Equation-of-State (EoS), which are capable of accurately predicting the complicated phase behavior. Three common tools of pressure–temperature (P–T), pressure–composition (P–X) and pseudoternary diagrams are used to display and analyze the alteration of phase behavior and types of displacement mechanism. Simulation results show that H2S is favorable to attain miscibility while CH4 and N2 are adverse, and the former can reduce the Multiple Contact Miscibility (MCM) pressure by the maximum level of 1.675 MPa per 0.1 mol. In addition, the phase envelope of the mixtures CO2/H2S displacing the reservoir oil on the pseudoternary diagram behaves a triangle shape, indicating the condensing-dominated process. While most phase envelopes of CO2/CH4 and CO2/N2 exhibit the trump and bell shapes, revealing the MCM of vaporization