Long term water integration in interconnected ramets of stoloniferous grass, buffalograss

Buffalograss (Buchloë dactyloides) is known for its drought resistant character. Natural resources are patchily distributed and resource sharing between interconnected ramets can enhance the performance of the whole genet. In order to examine whether there exists long term water integration between interconnected ramets of buffalograss, a greenhouse experiment was conducted. Interconnected ramet pairs of stoloniferous buffalograss were planted in two partitioned similar-sized containers and subjected to homogeneous (20 ml pot-1 d-1 or 100 - 150 ml pot-1 d-1) or heterogeneous (20 ml pot-1 d-1 vs. 100-150 ml pot-1 d-1) water supply; the whole experiment lasted for 91 days. In heterogeneous treatment, water translocation was equally effective in acropetal and basipetal directions. Elder ramet was more efficient in water use, but rooted ramet of elder ramet in moist condition experienced significant cost when it was connected to younger ramet in dry condition; whereas, no cost was found in any fragment of younger donor ramet. Ramet in dry condition produced more biomass than its connected ramet in moist condition and developed larger leaves. This “oversharing” phenomenon indicated that no net cost was involved in water integration, and water might not be the only resources transported within stolon xylem. Overall, long term water integration is an important strategy for buffalograss to cope with adverse natural drought conditions.Keywords: Water integration, interconnected ramets, heterogeneous treatment, Buchloë dactyloides, oversharingAfrican Journal of Biotechnology Vol. 9(34), pp. 5503-5510, 23 August, 201

Chronology of the Basalt Units Surrounding Chang’e-4 Landing Area

The Chang’e-4 (CE-4) lunar probe, the first soft landing spacecraft on the far side of the Moon, successfully landed in the Von Kármán crater on 3 January 2019. Geological studies of the landing area have been conducted and more intensive studies will be carried out with the in situ measured data. The chronological study of the maria basalt surrounding the CE-4 landing area is significant to the related studies. Currently, the crater size-frequency distribution (CSFD) technique is the most popular method to derive absolute model ages (AMAs) of geological units where no returned sample is available, and it has been widely used in dating maria basalt on the lunar surface. In this research, we first make a mosaic with multi-orbital Chang’e-2 (CE-2) images as a base map. Coupled with the elevation data and FeO content, nine representative areas of basalt units surrounding the CE-4 landing area are outlined and their AMAs are derived. The dating results of the nine basalt units indicate that the basalts erupted from 3.42 to 2.28 Ga ago in this area, a period much longer than derived by previous studies. The derived chronology of the above basalt units establishes a foundation for geological analysis of the returned CE-4 data

Local difference-based active contour model for medical image segmentation and bias correction

This study proposes a local bias field and difference estimation (LBDE) model for medical image segmentation and bias field correction. Firstly, the LBDE model uses a linear combination of a given set of smooth orthogonal basis functions, which is called Chebyshev polynomial, to estimate the bias field. Then, a clustering criterion function is defined by considering the difference between the measured image and approximated image in a small region. By applying this difference in the local region, the LBDE model can obtain accurate segmentation results and estimation of the bias field. Finally, the energy functional is incorporated into a level set formulation with a regularisation term, and it is minimised via the level set evolution process. The LBDE model first appears as a two-phase model and then extends to the multi-phase one. Extensive experiments on medical images demonstrate that the LBDE model achieves more precise segmentation results in terms of Jaccard similarity and dice similarity coefficient than the comparative models. Therefore the proposed model can increase the segmentation accuracy and robustness to noise

Study on Performance Evaluation and Prediction of Francis Turbine Units Considering Low-Quality Data and Variable Operating Conditions

The stable operation of the Francis turbine unit (FTU) determines the safety of the hydropower plant and the energy grid. The traditional FTU performance evaluation methods with a fixed threshold cannot avoid the influence of variable operating conditions. Meanwhile, anomaly samples and missing values in the low-quality on-site data distort the monitoring signals, which greatly affects the evaluation and prediction accuracy of the FTU. Therefore, an approach to the performance evaluation and prediction of the FTU considering low-quality data and variable operating conditions is proposed in this study. First, taking the variable operating conditions into consideration, a FTU on-site data-cleaning method based on DBSCAN is constructed to adaptively identify the anomaly samples. Second, the gate recurrent unit with decay mechanism (GRUD) and the Wasserstein generative adversarial network (WGAN) are combined to propose the GRUD–WGAN model for missing data imputation. Third, to reduce the impact of data randomness, the healthy-state probability model of the FTU is established based on the GPR. Fourth, the prediction model based on the temporal pattern attention–long short-term memory (TPA–LSTM) is constructed for accurate degradation trend forecasting. Ultimately, validity experiments were conducted with the on-site data set of a large FTU in production. The comparison experiments indicate that the proposed GRUD–WGAN has the highest accuracy at each data missing rate. In addition, since the cleaning and imputation improve the data quality, the TPA–LSTM-based performance indicator prediction model has great accuracy and generalization performance

Comparison of Radical Nephroureterectomy and Partial Ureterectomy for the Treatment of Upper Tract Urothelial Carcinoma

This study aimed to compare the oncological and renal outcomes of partial ureterectomy (PU) versus radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). UTUC patients’ clinical information was reviewed, and progression-free survival (PFS), overall survival (OS), and kidney function were collected. The mean follow-up period was 59 (6–135) months in the RNU group and 34.5 (5–135) months in the PU group. The mean operation time in the PU group was 141 (64–340) min, which is significantly shorter than the RNU group (P0.05). Multifactor Cox regression analysis indicated that age and the preoperative CKD stages were independent risk factors for poor kidney functions of UTUC patients. Compared to patients in RNU group, patients in PU have no significant difference in survival time but have shorter operation time, shorter hospital stay, and improved kidney functions

A review of the application of data-driven technology in shale gas production evaluation

Shale gas, as an important unconventional natural gas resource, is the main force to increase natural gas reserves and production in the future. For shale gas with huge resources, it is particularly important to accurately evaluate its development potential and realize scale benefit development. Given the importance of shale gas production evaluation in the context of digital transformation of the oil and gas industry, this paper presents a systematic review and examination of the application of data-driven technology in shale gas production evaluation to provide an overview of their current status. With the deepening of shale gas development theory and the maturity of data-driven technology, the existing data-driven technology has made great progress in shale gas production evaluation. It is worth noting that the application of these technologies in actual production needs to be further refined to assess shale gas production more accurately in various production demand scenarios, thereby effectively guiding production optimization and development plans. Therefore, in view of the limitations of the application of data-driven technology in shale gas production evaluation, this paper puts forward some possible priorities and trends in the future, aiming to optimize the application of data-driven technology in shale gas production evaluation in the future as much as possible

Evaluation of health-related quality of life using EQ-5D in China during the COVID-19 pandemic

OBJECTIVE: Since December 2019, an increasing number of cases of the 2019 novel coronavirus disease (COVID-19) infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified in Wuhan, Hubei Province, China. Now, more cases have been reported in 200 other countries and regions. The pandemic disease not only affects physical health who suffered it, but also affects the mental health of the general population. This study aims to know about the impact of the COVID-19 epidemic on the health-related quality of life (HRQOL) of living using EQ-5D in general population in China. METHODS: An online-based survey was developed and participants were recruited via social media. The questionnaires included demographic and socioeconomic data, health status, the condition epidemic situation and EQ-5D scale. The relationships of all factors and the scores of EQ-5D were analyzed. Logistic regression model were used to the five health dimensions. RESULTS: The respondents obtained a mean EQ-5D index score of 0.949 and a mean VAS score of 85.52.The most frequently reported problem were pain/discomfort (19.0%) and anxiety/depression (17.6%). Logistic regression models showed that the risk of pain/discomfort and anxiety/depression among people with aging, with chronic disease, lower income, epidemic effects, worry about get COVID-19 raised significantly. CONCLUSION: The article provides important evidence on HRQOL during the COVID-19 pandemic. The risk of pain/discomfort and anxiety/depression in general population in China raised significantly with aging, with chronic disease, lower income, epidemic effects, worried about get COVID-19 during the COVID-19 pandemic. The results from each categorical data can be used for future healthcare measures among general population