ABSTRACT
In this study, a connected waterflow watershed system in the Poyang Lake area was selected as the study site, which ranged from the primary tributary to the lake area (Xiangxi River Jiazhu River Ganjiang River Poyang Lake). The aims of the study were to monitor different forms of C and N and evaluate the transport flux of C and N, and then, the transport mechanisms of C and N and the variation characteristics of water quality parameters in Poyang Lake were discussed, with the intent of providing a scientific basis for the comprehensive management of watershed health within the Poyang Lake Basin ecosystem. The main results were as follows. â The concentrations of C and N in the Poyang Lake watershed exhibited significant seasonal changes, wherein the TIC, TOC, and TC concentrations in the Poyang Lake Basin were higher in the wet season than those in the dry season, and the NO3--N and DTN concentrations were higher in the dry season than those in the wet season. The main reason for the increase of TC in the wet season was the increase of TIC. Most of the TN in the wet season was transported by non-dissolved forms of N, while the TN in the dry period mostly was transported by DTN, and the DTN was mostly in the form of NO3--N. â¡ The C and N transport fluxes in the Poyang Lake watershed also showed significant seasonal variation. The C transport flux of Xiangxi River was lower during the wet season than that during the dry season, and the C transport flux of Jiazhu River and Ganjiang River was higher during the wet season than that during the dry season. The various forms of N transport flux in Xiangxi River, Jiazhu River, and Ganjiang River watershed were higher in the wet season than those in the dry season. There was a very significant positive correlation between the flux and runoff at the 99% confidence level. ⢠The COND, TDS, and pH in the Poyang Lake watershed were lower during the wet season than those during the dry season, while the ORP in the wet season was higher than that in the dry season.
ABSTRACT
In this study, by monitoring carbon transportation and its rainfall-runoff process during the rainy season in Loess Plateau's Yangjuangou dam watershed, we analyzed changes in carbon transportation driven by rainfall and further evaluated the C loss flux for the dam watershed. Results showed that the monthly C wet deposition flux for the wet and dry seasons were 3.33 kg·hm-2and 2.18 kg·hm-2, respectively, which were only small contributions to C transportation for the watershed. C transportation under the rainfall-runoff process in this watershed can reach 944.89 kg·km-2 and 300.29 kg·km-2 in August and September, respectively. Different intensities of rainfall runoff lead to different C loss processes, wherein dissolved inorganic carbon (DIC) is the main C form. Under small rainfall events, the output of dissolved total carbon (DTC) in this watershed was 156.98 kg·km-2; and the output of moderate rainfall events was 284.60 kg·km-2. Finally, we determined that the C loss modulus of the Yangjuangou watershed was 1.89 kg·(km2·mon)-1 in the rainy season, thus the C loss modulus for the study area could reach 2.70 kg·(km2·a)-1.
ABSTRACT
Induction of common knowledge or regularities from large-scale clinical data is a vital task for Chinese medicine (CM). In this paper, we propose a data mining method, called the Symptom-Herb-Diagnosis topic (SHDT) model, to automatically extract the common relationships among symptoms, herb combinations and diagnoses from large-scale CM clinical data. The SHDT model is one of the multi-relational extensions of the latent topic model, which can acquire topic structure from discrete corpora (such as document collection) by capturing the semantic relations among words. We applied the SHDT model to discover the common CM diagnosis and treatment knowledge for type 2 diabetes mellitus (T2DM) using 3 238 inpatient cases. We obtained meaningful diagnosis and treatment topics (clusters) from the data, which clinically indicated some important medical groups corresponding to comorbidity diseases (e.g., heart disease and diabetic kidney diseases in T2DM inpatients). The results show that manifestation sub-categories actually exist in T2DM patients that need specific, individualised CM therapies. Furthermore, the results demonstrate that this method is helpful for generating CM clinical guidelines for T2DM based on structured collected clinical data.
