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Convenient and accurate blood pressure (BP) measurement is of great importance in both clinical and daily life. Although deep learning (DL) can achieve cuff-less BP measurement based on Photoplethysmography (PPG), the performance of DL is affected by few-shot data. Data augmentation becomes an effective way to enhance the size of the training data. In this paper, we use cropping, flipping, DTW barycentric averaging (DBA), generative adversarial network (GAN) and variational auto-encoder (VAE) for the data augmentation of PPG. Furthermore, a PE–CNN–GRU model is designed for cuff-less BP measurement applying position encoding (PE), convolutional neural networks (CNNs) and gated recurrent unit (GRU) networks. Experiment results based on real-life datasets show that VAE is the most suitable method for PPG data augmentation, which can reduce the mean absolute error (MAE) of PE–CNN–GRU when measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 18.80% and 19.84%. After the data augmentation of PPG, PE–CNN–GRU achieves accurate and cuff-less BP measurement, thus providing convenient support for preventing cardiovascular diseases. [ABSTRACT FROM AUTHOR]

The reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long‐term IA temporal variability remain unknown. Here, 1861 county‐level IA information from Government Censored Data (GCD) during 2000–2021 are collected, cross‐validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter‐annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD‐based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms. Plain Language Summary: Understanding how much land is irrigated (Irrigated Area or IA) is crucial for accurate water use and crop modeling. Traditional IA maps are developed using data from the Food and Agriculture Organization (FAO) and remote sensing indices. Recent improvements, like machine learning, have enhanced IA mapping. However, the relative performances of these methods and their reliability in capturing long‐term changes in irrigated areas remain unknown. This study collects government‐censored county‐level IA data from 2000 to 2021, which is subsequently used for evaluating commonly used IA data sets. Results show that data sets directly using FAO data can reasonability capture the spatial distribution of IA but may miss year‐to‐year changes. On the other hand, data sets relying only on vegetation data tend to overestimate irrigated areas in humid regions. The latest machine learning‐based IA data sets are relatively more accurate but still contain significant uncertainties in IA trends. These biases are closely linked to agricultural suitability (calculated using precipitation and potential evapotranspiration), which may be useful for enhancing future IA mapping algorithms. Key Points: Remote sensing vegetation index is insufficient for identifying irrigated area over humid regionsMachine learning based irrigated area is sensitive to the quality of training sampleAll the irrigated area data sets are biased in representing the temporal dynamics of irrigated area [ABSTRACT FROM AUTHOR]

Objectives: To explore the mediating role of self‐esteem in social anxiety and QoL during the COVID‐19 local epidemic in China. Design: A cross‐sectional survey. Methods: This study conveniently selected 344 hypertensive patients from a Grade‐A tertiary hospital in Wuhu, Anhui Province, as the study population. Participants completed the demographic questionnaire, the social phobia inventory, the self‐esteem scale and the 36‐item short‐form health survey. Results: Social anxiety, self‐esteem and QoL were significantly correlated with each other. Social anxiety showed no direct effect on QoL (β = 0.011, p > 0.05). Social anxiety showed an indirect effect on QoL (β = −0.248, p < 0.001). Self‐esteem fully mediated the association between social anxiety and QoL in hypertensive patients. Patient or Public Contribution: Hypertensive patients in this study were participants during the data collection process. Nursing staff from the cardiology department at the hospital of a hospital in Wuhu City, Anhui Province assisted in the recruitment phase of the data collection process. [ABSTRACT FROM AUTHOR]

The natural language to SQL (NL2SQL) task is an emerging research area that aims to transform a natural language with a given database into an SQL query. The earlier approaches were to process the input into a heterogeneous graph. However, previous models failed to distinguish the types of multi-hop connections of the heterogeneous graph, which tended to ignore crucial semantic path information. To this end, a two-layer attention network is presented to focus on essential neighbor nodes and mine enlightening semantic paths for feature encoding. The weighted edge is introduced for schema linking to connect the nodes with semantic similarity. In the decoding phase, a rule-based pruning strategy is offered to refine the generated SQL queries. From the experimental results, the approach is shown to learn a good encoding representation and decode the representation to generate results with practical meaning. [ABSTRACT FROM AUTHOR]

Amorphous high-entropy alloys (HEAs) as electromagnetic-wave absorbing materials have been rarely reported. In this work, amorphous FeCoNiTixSi HEAs were synthesized by introducing a high content of large-atom Ti using the high-energy ball-milling technique. This amorphous structure could improve the saturated magnetization and coercivity of HEAs, but slightly degraded the mechanical and oxidation resistance properties. In terms of electromagnetic properties, FeCoNiTi0.01Si and FeCoNiTiSi exhibit excellent electromagnetic-wave absorption performances, with significant absorptions of −68.4 dB at 6.14 GHz and −63.4 dB at 9.12 GHz, corresponding to bandwidths of 5.15 GHz (1.69 mm) and 3.64 GHz (1.43 mm), respectively. Overall, the prepared FeCoNiTixSi HEAs exhibited superior comprehensive performances compared to other HEA absorption materials. This work provided a novel strategy for the development of new electromagnetic-wave absorption materials with low weight, high absorption efficiency, and resistance to harsh environments. [ABSTRACT FROM AUTHOR]