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In this paper, a combination of Problem-based Learning (PBL), Case-based Learning(CBL)and Flipped Classroom was used to conduct teaching ward round of nursing for a patient with thyroid disease. Taking the clinical teaching round of a patient with stony goiter as an example, this paper explored the transformation from the conventional clinical teaching method of nursing to the comprehensive application of multi-mode joint of clinical teaching. Focusing on the nursing students, the multi-mode joint model was a problem-oriented and case-based approach. It constructed a new type of teaching ward round of integrated traditional Chinese and western medicine nursing by taking clinical thinking ability as motif, disease related knowledge as the support, and teacher-student and nurse-patient interaction as means. It provides theoretical and practical basis for comprehensively improving the quality of nursing work, teaching quality and patient satisfaction, and exploring the cultivation of high-quality comprehensive Traditional Chinese Medicine nursing talents. (本文探讨运用以问题为基础学习(PBL)、以案例为基础学习(CBL)和翻转课堂的“三轨”联合教学进行护理教学查房的效果。以普外科1例石瘿患者的临床教学查房为例, 探索从传统单一的护理临床教学方法向多模式融合的综合应用的临床教学转变, 以实习护生为主体, 以问题为导向, 以病例为基础, 以中医临床思维为主线, 以疾病相关知识为支撑, 以师生、护患互动为手段, 形成“三轨”联合教学模式, 从而构建一种新型的中西医结合护理临床教学查房模式, 为全面提升护理工作质量、教学质量及患者满意度, 探索培养高素质综合型中医护理人才提供理论及实践依据。)

Alpine land cover (ALC) is facing many challenges with climatic change, biodiversity reduction and other cascading ecosystem damage triggered by natural and anthropogenic interference. Although several global land cover products and thematic maps are already available, their mapping accuracy of alpine and montane regions remains unsatisfactory due to the data acquisition, methodology, and workflow design constraints. Therefore, in this paper, a deep convolutional neural network (DCNN) in Google Earth Engine (GEE) was developed to map the ALC types of the Yarlung Zangbo river basin (YZRB) in the Tibetan plateau using multi-source remote sensing data. The DCNN algorithm was offline trained using automatically generating samples and online deployed in the GEE for a large-scale ALC mapping. Moreover, a set of fine land cover classification system (containing 14 ALC types) was also established in accordance with the natural situation of the YZRB. The overall accuracy and kappa were 86.24% and 0.8156, which were higher than traditional classification algorithms. The spatial distribution of ALC types was analyzed in different gradient zones, and a clear altitudinal characteristic was noticed. The terrain of the YZRB from upper-stream to down-stream with an elevation dramatically decreases, and corresponding to vertical zonal changes from glacier and permanent snow/ice, barren gravel land, alpine desert steppe, alpine steppe, alpine meadow, shrubs, to tree cover. The product can provide valuable land cover information to support alpine ecosystem conservation.

A dual experimental/theoretical investigation of the Ireland-Claisen rearrangement of tetrasubstituted α-phthalimido ester enolates to afford α-tetrasubstituted, β-trisubstituted α-amino acids (generally >20:1 dr) is described. For trans allylic olefins, the Z- and E-enol ethers proceed through chair and boat transition states, respectively. For cis allylic olefins, the trend is reversed. As a result, the diastereochemical outcome of the reaction is preserved regardless of the geometry of the enolate or the accompanying allylic olefin. We term this unique convergence of all possible olefin isomers global diastereoconvergence. This reaction manifold circumvents limitations in present-day technologies for the stereoselective enolization of α,α-disubstituted allyl esters. Density functional theory paired with state-of-the-art local coupled-cluster theory (DLPNO-CCSD(T)) was employed for the accurate determination of quantum mechanical energies.

This paper summarized the nursing management for a case of hyperemesis gravidarum treated by acupoint pressing combined with acupoint Application of Traditional Chinese Medicine (TCM). Key issues of nursing mainly included acupoint selection, pressing technique, Chinese herbal medicine ointment making and precautions during treatment. In addition, life guidance, diet care, social support and psychological intervention were carried out jointly to reduce the frequency of vomiting and improve patient’s quality of life. (本文总结穴位按压联合中药穴位贴敷治疗1例妊娠剧吐患者的护理经验, 主要包括取穴、穴位按压、中药药膏调制以及注意事项等, 同时配合生活指导、饮食调护、社会支持、心理干预等护, 减少患者呕吐次数, 提升患者生活质量。)

Abstract Background Tea trees originated in southwest China 60 million or 70 million years ago. Written records show that Chinese ancestors had begun drinking tea over 3000 years ago. Nowadays, with the aging of populations worldwide and more people suffering from non-communicable diseases or poor health, tea beverages have become an inexpensive and fine complementary and alternative medicine (CAM) therapy. At present, there are 3 billion people who like to drink tea in the world, but few of them actually understand tea, especially on its development process and the spiritual and cultural connotations. Methods We searched PubMed, Google Scholar, Web of Science, CNKI, and other relevant platforms with the key word “tea”, and reviewed and analyzed tea-related literatures and pictures in the past 40 years about tea’s history, culture, customs, experimental studies, and markets. Results China is the hometown of tea, tea trees, tea drinking, and tea culture. China has the oldest wild and planted tea trees in the world, fossil of a tea leaf from 35,400,000 years ago, and abundant tea-related literatures and art works. Moreover, tea may be the first Chinese herbal medicine (CHM) used by Chinese people in ancient times. Tea drinking has many benefits to our physical health via its antioxidant, anti-inflammatory, immuno-regulatory, anticancer, cardiovascular-protective, anti-diabetic, and anti-obesity activities. At the moment, COVID-19 is wreaking havoc across the globe and causing severe damages to people’s health and lives. Tea has anti-COVID-19 functions via the enhancement of the innate immune response and inhibition of viral growth. Besides, drinking tea can allow people to acquire a peaceful, relaxed, refreshed and cheerful enjoyment, and even longevity. According to the meridian theory of traditional Chinese medicine, different kinds of tea can activate different meridian systems in the human body. At present, black tea (fermented tea) and green tea (non-fermented tea) are the most popular in the world. Black tea accounts for over 90% of all teas sold in western countries. The world’s top-grade black teas include Qi Men black in China, Darjeeling and Assam black tea in India, and Uva black tea in Sri Lanka. However, all top ten famous green teas in the world are produced in China, and Xi Hu Long Jing tea is the most famous among all green teas. More than 700 different kinds of components and 27 mineral elements can be found in tea. Tea polyphenols and theaflavin/thearubigins are considered to be the major bioactive components of black tea and green tea, respectively. Overly strong or overheated tea liquid should be avoided when drinking tea. Conclusions Today, CAM provides an array of treatment modalities for the health promotion in both developed and developing countries all over the world. Tea drinking, a simple herb-based CAM therapy, has become a popular man-made non-alcoholic beverage widely consumed worldwide, and it can improve the growth of economy as well. Tea can improve our physical and mental health and promote the harmonious development of society through its chemical and cultural elements.

Accurate monitoring of river discharge variations is essential for managing floods and droughts and understanding the response of global river systems to climate change. Remote sensing of discharge (RSQ) offers a timely and efficient alternative for widespread monitoring, particularly in ungauged areas. Current methods often struggle with accuracy, especially when estimating the width of narrow rivers from medium‐resolution images. We first observe that, although estimating the width variation of narrow rivers can be challenging from medium‐resolution satellite imagery, river discharge still correlates with river surface color or reflectance. However, existing methods can only correlate river surface reflectance with discharge in gauged rivers. Here, we introduce a novel method employing an advanced Transformer architecture to map river discharge variations directly from time‐series reflectance imagery. Our model, trained on quality‐checked data from 2,036 discharge gauges, outperforms existing methods in discharge estimation accuracy and is less affected by the need for precise river width estimation. The proposed model yields positive Kling‐Gupta Efficiency (KGE) in 68.6% of ungauged rivers, a substantial improvement over the BAM and geoBAM methods, which show positive KGEs in only 28.4% and 33.1% of rivers, respectively. Notably, this performance is achieved despite two‐thirds of the rivers being less than 100 m wide, a range where traditional RSQ methods typically struggle, and the RSQ performance does not show degradation for braided rivers. Our approach suggests a significant shift toward more efficient, extensive, and adaptable space‐based river discharge monitoring. Plain Language Summary: Monitoring river discharge is crucial for managing water‐related disasters like floods and droughts and for understanding how rivers respond to climate change. Traditional methods that use remote sensing to monitor discharge (RSQ) often face challenges, especially with accurately measuring the width of narrow rivers using satellite images. However, we've noticed that the color or reflectance of a river's surface, which can be seen from space, still tells us something about the river's discharge, even if the width is hard to measure accurately. Our study introduces a new method using a Transformer architecture that learns to estimate river discharge directly from how the river looks in time‐series satellite images. We trained our model using data from 2,036 river gauges and found that it performs better than existing methods. It is particularly effective in areas where we don't have direct river discharge measurements, achieving good results in 68.6% of these cases‐a big improvement over the 28.4% and 33.1% success rates of previous methods. This is impressive because two‐thirds of the rivers we studied are less than 100 m wide, where older methods usually fail. Key Points: Deep learning model maps river discharge from satellites in ungauged rivers, improving accuracy for rivers over 30 m wideThe model uses Transformer architecture, correlating river surface reflectance data with discharge when width changes are minimalValidated with positive Kling‐Gupta Efficiency in 68.6% of ungauged rivers, median width 67 m, significantly better than existing Mass Conserved Flow Law Inversion (McFLI) methods [ABSTRACT FROM AUTHOR]

Many semi-distributed models that simulate pollutants' losses from watersheds do not handle well detailed spatially distributed and temporal data with which to identify accurate and cost-effective strategies for controlling pollutants issuing from non-point sources. Such models commonly overlook the flow pathways of pollutants across the landscape. This work aims at closing such knowledge gap by developing a Spatially and Temporally Distributed Empirical model for Phosphorus Management (STEM-P) that simulates the daily phosphorus loss from source areas to receiving waters on a spatially-distributed grid-cell basis. STEM-P bypasses the use of complex mechanistic algorithms by representing the phosphorus mobilization and delivery processes with zero-order mobilization and first-order delivery, respectively. STEM-P was applied to a 217km2 watershed with mixed forest and agricultural land uses situated in southwestern China. The STEM-P simulation of phosphorus concentration at the watershed outlet approximated the observed data closely: the percent bias (Pbias) was -7.1%, with a Nash-Sutcliffe coefficient (ENS) of 0.80 on a monthly scale for the calibration period. The Pbias was 18.1%, with a monthly ENS equal to 0.72 for validation. The simulation results showed that 76% of the phosphorus load was transported with surface runoff, 25.2% of which came from 3.4% of the watershed area (classified as standard A critical source areas), and 55.3% of which originated from 17.1% of the watershed area (classified as standard B critical source areas). The standard A critical source areas were composed of 51% residences, 27% orchards, 18% dry fields, and 4% paddy fields. The standard B critical source areas were mainly paddy fields (81%). The calculated spatial and temporal patterns of phosphorus loss and recorded flow pathways identified with the STEM-P simulations revealed the field-scale critical source areas and guides the design and placement of effective practices for non-point source pollution control and water quality conservation.