06-87.46 nT/√Hz and 5.043-7.5 ng/√Hz. The resolution was limited by circuit noise equivalent acceleration (CNEM) and Brownian noise equivalent magnetic field (BNEM).Simulation-based communication education has improved nursing students' communication knowledge and skills. However, communication patterns that students commonly exhibit in simulated situations and students' responses to specific clinical situations have not been systematically examined. The specific aims of the present study were (1) to identify non-therapeutic communication patterns that nursing students exhibit in simulated situations in the computer simulation-based education (ComEd) program, and (2) explore students' responses to challenging clinical situations. This study used a mixed-method research design and a convenience sampling method to recruit participants. Frequency analysis and a conventional content analysis method were used to analyze answers provided by participants. A total of 66 students from four Korean nursing schools participated in the study. "False reassurance" was found to be the most common non-therapeutic communication pattern used by nursing students. Nursing students had difficulty in clinical situations such as reporting a patient's condition to a doctor, communicating with a patient and perform basic nursing skills at the same time, and managing conflicts between patients. Technology-based communication simulation programs, which reflect various clinical situations, are considered a new alternative that can supplement the limitations of clinical practicum and improve the quality of nursing education.Both protease- and reactive oxygen species (ROS)-mediated proteolysis are thought to be key effectors of tissue remodeling. We have previously shown that comparison of amino acid composition can predict the differential susceptibilities of proteins to photo-oxidation. However, predicting protein susceptibility to endogenous proteases remains challenging. Here, we aim to develop bioinformatics tools to (i) predict cleavage site locations (and hence putative protein susceptibilities) and (ii) compare the predicted vulnerabilities of skin proteins to protease- and ROS-mediated proteolysis. The first goal of this study was to experimentally evaluate the ability of existing protease cleavage site prediction models (PROSPER and DeepCleave) to identify experimentally determined MMP9 cleavage sites in two purified proteins and in a complex human dermal fibroblast-derived extracellular matrix (ECM) proteome. We subsequently developed deep bidirectional recurrent neural network (BRNN) models to predict cleavage sites for 14 tissue proteases. The predictions of the new models were tested against experimental datasets and combined with amino acid composition analysis (to predict ultraviolet radiation (UVR)/ROS susceptibility) in a new web app the Manchester proteome susceptibility calculator (MPSC). The BRNN models performed better in predicting cleavage sites in native dermal ECM proteins than existing models (DeepCleave and PROSPER), and application of MPSC to the skin proteome suggests that compared with the elastic fiber network, fibrillar collagens may be susceptible primarily to protease-mediated proteolysis. We also identify additional putative targets of oxidative damage (dermatopontin, fibulins and defensins) and protease action (laminins and nidogen). MPSC has the potential to identify potential targets of proteolysis in disparate tissues and disease states.Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. https://www.selleckchem.com/products/linderalactone.html A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e., X = -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.Power plants are considered as the major source of carbon dioxide pollution in Kuwait. The gas is released from the combustion of fuel with air to convert water into steam. It has been proven that the use of enriched oxygen can reduce fuel consumption and minimize emissions. In this study, UniSim (Honeywell, Charlotte, NC, USA) was used to estimate the fuel savings and carbon dioxide emissions of the largest power plant in Kuwait (Alzour). Results showed that at 30 mol% oxygen, the fuel consumption was lowered by 8%, with a reduction in carbon dioxide emissions by 3524 tons per day. An economic analysis was performed on the use of a membrane unit to produce 30 mol% oxygen. At current market prices, the unit is not economical. However, the system can achieve a payback duration of 3 years if natural gas price increases to USD 6.74 or the compressor cost decreases to USD 52 per kW. Currently, the research and development sector is targeting a membrane fabrication cost of less than USD 10 per m2 to make the membrane process more attractive.