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  • Raw and processed data for the article: Analysis of air quality changes in Wuhan during COVID-19 by Grey relational analysis.
    Data Types:
    • Tabular Data
    • Dataset
  • This dataset refers to the data of 1244 people, aged from 18 to 72 years. Data concerns socio-demographic and epidemiological information and results to 3 questionnaires, as follows. The authors of the study used the information available on WHO website to examine knowledge about COVID-19. Eight questions were used to examine the participants knowledge about coronavirus, 5 of them were multiple choice questions and 3 questions were true/false type questions. Various types of factor and reliability analyzes were performed to see whether a linear combination of the results could be used, but the results did not support this. It was then decided to consider each question as separate, examining their relationship to the research variables. Participants were divided in two groups for each of the 8 questions according to their answers. More specifically, if a participant scored correctly on question 2, but incorrectly on question 3 he was put in the ‘informed’ group for question 2 and the ‘uninformed’ group for question 3. The The Hospital Anxiety and Depression scale (HADS) (Zigmond and Snaith, 1983:Pokrajac-Bulian et al. 2015) is divided in the Anxiety subscale and the Depression subscale. Both subscales contain 7 items. Participants were divided into three categories according to the authors. The Optimism-Pessimism scale (OPS) (Penezić,2000) was to measure positive and negative expectations of future activities outcome. This scale consists of the Optimism subscale which has 6 items and the Pessimism subscale which has 8 items.
    Data Types:
    • Tabular Data
    • Dataset
  • The deposition of atmospheric nitrate (NO3−) reflects a balance of anthropogenic and natural nitrogen oxide (NOX) sources. The Tibetan Plateau, one of the most pristine global areas, is highly sensitive to anthropogenic effects, thus is of great importance for evaluating human activity disturbance on natural system. However, the current sources of nitrate deposition on the Tibetan Plateau are still poorly constrained. In this study rainwater from the Gongga Mountain, which is located in the eastern Tibetan Plateau, was collected from Aug. 2014 to Aug. in 2015, and the chemical and nitrogen isotopic compositions were determined. The δ15N composition of nitrate in rainwater was found to be high during the early cool season (November to January: −2.8‰), and low during the late cool season (February to March: −15.3‰), with intermediate values during the warm season (April to October: −5.8‰). In the warm season, trajectory paths and similar intermediate δ15N values imply that sea aerosol from the Indian Ocean as the main source of the precipitation nitrate. In the cool season, variable δ15N values suggest a more complicated and dynamic control on precipitation nitrate in the eastern Tibetan Plateau. In the early cool season, anthropogenic NOX derived from fuel combustion in the Indian sub-continent are inferred to dominate the precipitation NOX in the study site, based on high K+ concentration and nitrogen isotopic value and on the trajectory analyses. The increased intensity of agricultural activity in the Indian sub-continent during the late cool season shifts the δ15N values lower and drives a peak of NH4+ and NO3− concentration of the precipitation. These findings represent the first constraints on the origin of precipitation nitrate in the Tibetan Plateau and demonstrates that the Tibetan Plateau responds quickly to anthropogenic nitrate derived by long−range transport from the Indian sub-continent.
    Data Types:
    • Tabular Data
    • Dataset
  • IDL procedures and data files used for doing AYS calculations and generating figures in the paper.
    Data Types:
    • Dataset
    • Text
  • The data is for a gravity model for East African Community Partner States from 1996 to 2018. It's for an estimation of intra-EAC trade.
    Data Types:
    • Tabular Data
    • Dataset
  • The folder contains the dataset in Fujiyama et al., Cell Reports 2018. Descriptions and technical explanations are reported in the paper.
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
  • The VBA codes are embedded in the developer of .xlsm files. 'HTE_PL_ABS.xlsm' to extract and sort the raw data of PL/Abs. at different aging time. 'HTE_ABS.xlsm' to analyze the absorption data. 'HTE_PL.xlsm' to sort the PL data. 'HTE_summary.xlsm' to summarize the T80 lifetime.
    Data Types:
    • Tabular Data
    • Dataset
  • Retroscpective review of outcomes of laparoscopic Sacrocolpopexy over an extended period of time with Qol questionnaire
    Data Types:
    • Tabular Data
    • Dataset
  • Study question Is it effective for patients taking dienogest to use progestin-primed ovarian stimulation (PPOS) protocol during controlled ovarian hyperstimulation (COH), compared to PPOS with dydrogesterone (DYG)? Study answer Patients taking dienogest can continue the endometriosis treatment and get good quality embryo using PPOS during COH, despite they have severe ovarian endometriosis. What is known already Dienogest is an oral progestin effective for the treatment of symptomatic endometriosis, such as reduction of endometrial lesion and control of pain intensity with safe profile and good tolerability. Dienogest also provides complete ovulation inhibition at a daily dose of 2mg, and a rapid recovery of ovarian function after cessation of its administration. PPOS is a new COH regimen using a progestin as alternative to GnRH antagonist for blocking LH surge, and several reports have shown that DYG is an appropriate progestin for PPOS protocol. However, dienogest has not been used in PPOS protocol yet. Study design, size, duration This was a prospective controlled study of 145 women with endometriosis (aged <41) undergoing COH for IVF/ICSI and frozen embryo transfer (FET) at our infertility center from February in 2018 to November in 2019. The patients taking dienogest were allocated in Study group, and the other patients taking PPOS with DYG were allocated in Control group. Participants/ Materials, setting, methods A total of 145 patients were analyzed, PPOS with DNG: 71 patients, PPOS with DYG: 74 patients. Of the participants, 111 patients were histologically confirmed as endometriosis and 39 patients were diagnosed with published imaging criteria using transvaginal ultrasonography, respectively. Patients took DNG 2mg continuously in DNG group, and DYG were started day 3 of COH cycle. Patients were administrated with 150-225 IU of human menopausal gonadotropin (hMG) daily for COH. All viable embryos were cryopreserved for later transfer. The primary outcome measure was the clinical pregnancy rate. Main Results The number of oocytes retrieved in DNG group was less than that of DYG group (6.18±3.60 vs. 9.85±5.77, P<0.001), however, the rate of mature oocytes in DNG group was significantly higher than in DYG group [89.1% (391/439) vs. 78.9% (575/729), P<0.001].The fertilization rate was comparable between the two groups (C-IVF; 69.0% for DNG group vs. 65.1% for DYG group, P=0.510, ICSI; 80.1% for DNG protocol vs. 78.2% for DYG group, P=0.558). The clinical pregnancy rate [Odds ratio (OR) 1.15, 95%CI: 0.69~1.94, P=0.579 ] :50.5% (54/107) for DNG group vs.46.8% (59/126) for DYG group. The ongoing pregnancy rate [OR 0.70, 95%CI: 0.45~1.61, P=0.323]:55.2% (37/67) for DNG group vs.63.6% (42/66) for DYG group did not differ between the two groups.
    Data Types:
    • Slides
    • Tabular Data
    • Dataset
  • First, this dataset contains the documentation of the APS-RA (Advanced Planning Systems Reference Architecture), which is a reference architecture to assist and simplify the development of Advanced Planning Systems (APS). These are specific Decision Support System that automates the optimization of the different organizational process, aiming to provide a user-friendly interface, while automatically interacting with existing enterprise systems. This documentation is provided as a PDF, following the "Views & Beyond" documentation style. Second, APS-RA has been evaluated using ATAM (Architecture Tradeoff Analysis Method); this process is a known method for assessing software architectures. However, this process was made in two stages, with different stakeholders. This decision was made in order to deal with intrinsic characteristics of both the APS domain and of reference architectures.
    Data Types:
    • Tabular Data
    • Dataset
    • Document
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