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The aim of this study is designed an improved ResNet 50 network to achieve automatic classification model for pain expressions by elderly patients with hip fractures. This study built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Based on ResNet50 network framework utilized transfer learning to implement model function. This study performed the hyperparameters by Bayesian optimization in the learning process. This study calculated intraclass correlation between visual analog scale scores provided by clinicians independently and those provided by pain expression evaluation assistant(PEEA). The automatic pain expression recognition model in elderly patients with hip fractures, which constructed using the algorithm. The accuracy achieved 99.6% on the training set, 98.7% on the validation set, and 98.2% on the test set. The substantial kappa coefficient of 0.683 confirmed the efficacy of PEEA in clinic. This study demonstrates that the improved ResNet50 network can be used to construct an automatic pain expression recognition model for elderly patients with hip fractures, which has higher accuracy.

Plant roots are sensitive to potassium (K+) deficiency signals. Therefore, regulating root growth by exogenous methods is a vital strategy to improve low K+ tolerance of sweetpotato. We studied the effects of exogenous indole-3-acetic acid (IAA) on growth, K+ absorption, and root characteristics in sweetpotato exposed to low K+ treatment (LK). LK significantly inhibited dry mass, K+ concentration and accumulation, as well as the root elongation (length) and branching (forks and crossings) in sweetpotato seedlings. However, exogenous IAA increased the length, ratio, and density of lateral roots and promoted absorption and accumulation of K+, which effectively alleviated the inhibitory effect of low K+. Exogenous IAA also increased the expression levels of auxin synthesis (IbYUC6 and IbTAR2) and transport (IbPIN1, IbPIN3, and IbPIN8) genes in leaves and roots, which promoted the increase of endogenous IAA content. Furthermore, exogenous IAA was more effective on low-K-tolerant variety (XS32) than low-K-sensitive variety (NZ1) under LK stress, depending on their different IAA synthesis and transport strategies. These results indicated that exogenous IAA enhanced root responsiveness of sweetpotato to low K+ stress by modulating auxin biosynthesis and transport, thereby improving the tolerance of sweetpotato to low K+ stress. [ABSTRACT FROM AUTHOR]

The photocatalytic performance of g-C3N4 could be effectively improved by reducing the recombination rate of photogenerated electron–hole pairs. In this work, g-C3N4 was combined with CoWO4 to construct Z-scheme heterojunction by ultrasonic impregnation method. The successful synthesis of CoWO4/g-C3N4 composites was confirmed by comprehensive characterization. Systematic studies showed that the well-matched band structures between them contributed to the formation of close interfacial contacts, which could effectively increase the transfer rate of photogenerated electrons. The photocatalytic degradation performance showed that the degradation efficiency of tetracycline (TC) reached 98.3% in 150 min under ultraviolet light, which was mainly due to the establishment of Z-scheme heterojunction. At the same time, CoWO4/g-C3N4 composites could still maintain good stability after four cycles. In addition, the possible photocatalytic mechanism of CoWO4/g-C3N4 composites was also proposed. This work provided an effective way for the preparation of efficient photocatalyst with the Z-scheme heterojunction. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has brought significant challenges to healthcare systems worldwide, prompting governments to allocate substantial resources toward public health spendings (PHS). However, the uncertainties surrounding the pandemic have raised questions about the effectiveness and sustainability of such expenditures. This research analyzes the nonlinear link between pandemic uncertainty (PNU) and PHS in countries with highest PNU (USA, India, France, Germany, UK, Saudi Arabia, South Korea, Indonesia, Japan, and China). Previous studies have employed panel data methodologies to establish consistent findings regarding the relationship between pandemics and health spendings, regardless of the fact that several countries have not autonomously recognized this connection. In contrast, this current research adopts a distinctive tool called "quantile‐on‐quantile," which enables the examination of time series dependency within each economy, providing both international and country‐specific perspectives on the relationship between the variables. The estimations indicate that PNU leads to an increase in PHS in the vast majority of economies chosen by us, focusing on definite segments of the data distribution. Moreover, the data demonstrates that there are differences in the asymmetry between the variables across various nations. This underscores the need for policymakers to take careful deliberation when formulating policies related to health spendings and addressing the challenges posed by pandemic uncertainty. [ABSTRACT FROM AUTHOR]

Open-pit mining activities inevitably affect the surrounding ecological environment. Therefore, it is crucial to clarify the disturbance characteristics of open-pit mining activities on the surrounding vegetation and scientifically implement ecological restoration projects. This study investigates the impact of open-pit coal mining in arid and semi-arid regions on surrounding vegetation from a vegetation phenology perspective. Initially, we construct a high-frequency time series of vegetation indices by Harmonized Landsat 8 and Sentinel-2 surface reflectance dataset (HLS). These time series are then fitted using the Double Logistic and Asymmetric Gaussian methods. Subsequently, we quantify three pivotal phenological phases: Start of Season (SOS), End of Season (EOS), and Length of Season (LOS) from the fitted time series. Finally, utilizing mine boundaries as spatial units, we create a buffer zone of 100 m increments to statistically analyze changes in phenological phases. The results reveal an exponential variation in vegetation phenological metrics with increasing distance from the mining areas of Heidaigou-Haerwusu (HDG-HEWS), Mengxiang (MX), and Xingda (XD) in northwest China. Then, we propose a method to identify the disturbance range. HDG-HEWS, MX, and XD mining areas exhibit disturbance ranges of 1485.39 m, 1571.47 m, and 671.92 m for SOS, and 816.72 m, 824.73 m, and 468.92 m for EOS, respectively. Mineral dust is one of the primary factors for the difference in the disturbance range. The HDG-HEWS mining area exhibits the most significant disruption to vegetation phenological metrics, resulting in a delay of 6.4 ± 3.4 days in SOS, an advancement of 4.3 ± 3.9 days in the EOS, and a shortening of 6.7 ± 3.5 days in the LOS. Furthermore, the overlapping disturbance zones of the two mining areas exacerbate the impact on phenological metrics, with disturbance intensities for SOS, EOS, and LOS being 1.38, 1.20, and 1.33 times those caused by a single mining area. These research results are expected to provide a reference for the formulation of dust suppression measures and ecological restoration plans for open-pit mining areas. [ABSTRACT FROM AUTHOR]