One of the compulsory procedures in any diagnostic process is the measurement of blood pressure (BP). The most accustomed method of measurement of blood pressure requires at least one person to do the recording. However, the integration of IoT and other sensing technology has transformed the way BP was previously monitored. For example, in [88], a wearable cuffless gadget has been proposed that can measure both systolic and diastolic pressure. The recorded information can be stored in the cloud. Further, the efficiency of this device was tested on 60 persons and the accuracy was validated. Guntha has implemented cloud computing and fog computing in the IoT-based BP measurement system [89]. This prepared the system for long-term real-time monitoring. The device could also store the recorded data for future references. In a similar study [90], a deep learning-based CNN model with time-domain characteristics was used for the evaluation of systolic and diastolic blood pressure. The measurement of BP using the ECG signal and photoplethysmogram (PPG), recorded from the fingertip, has been proposed in [91]. Herein, the BP was computed using the attached microcontroller module and then the recorded data were sent to the cloud storage.
Abundance surveys of a large sample of Galactic planetary nebulae (PNe) have led to the discovery of a group of super-metal-rich nebulae whose spectra show prominent optical recombination lines (ORLs) from C, N, O, and Ne ions. The heavy element abundances derived from ORLs for several PNe are a factor >10 higher than those derived from the traditional method based on collisionally excited lines (CELs). This ratio is called the abundance discrepancy factor (adf). A promising proposition to explain the nebular abundance problem posits that these nebulae contain (at least) two distinct regions - one of "normal" electron temperature, Te (10000 K) and chemical composition (solar) and another of very low Te (
NEW! Chemistry Form 5 Module Nilam Answer 13
To determine whether specific design interventions (changes in the user interface (UI)) of an electronic health record (EHR) medication module are associated with an increase or decrease in the incidence of contradictions between the structured and narrative components of electronic prescriptions (internal prescription discrepancies). We performed a retrospective analysis of 960,000 randomly selected electronic prescriptions generated in a single EHR between 01/2004 and 12/2011. Internal prescription discrepancies were identified using a validated natural language processing tool with recall of 76% and precision of 84%. A multivariable autoregressive integrated moving average (ARIMA) model was used to evaluate the effect of five UI changes in the EHR medication module on incidence of internal prescription discrepancies. Over the study period 175,725 (18.4%) prescriptions were found to have internal discrepancies. The highest rate of prescription discrepancies was observed in March 2006 (22.5%) and the lowest in March 2009 (15.0%). Addition of "as directed" option to the dropdown decreased prescription discrepancies by 195 / month (p = 0.0004). An non-interruptive alert that reminded providers to ensure that structured and narrative components did not contradict each other decreased prescription discrepancies by 145 / month (p = 0.03). Addition of a "Renew / Sign" button to the Medication module (a negative control) did not have an effect in prescription discrepancies. Several UI changes in the electronic medication module were effective in reducing the incidence of internal prescription discrepancies. Further research is needed to identify interventions that can completely eliminate this type of prescription error and their effects on patient outcomes.
This research evaluated the POWERFUL IDEAS IN PHYSICAL SCIENCE (PIiPS) curriculum model used to develop a physical science course taken by preservice elementary teachers. The focus was on the evaluation of discrepant events used to induce conceptual change in relation to students' ideas concerning heat, temperature, and specific heat. Both quantitative and qualitative methodologies were used for the analysis. Data was collected during the 1998 Fall semester using two classes of physical science for elementary school teachers. The traditionally taught class served as the control group and the class using the PIiPS curriculum model was the experimental group. The PIiPS curriculum model was evaluated quantitatively for its influence on students' attitude toward science, anxiety towards teaching science, self efficacy toward teaching science, and content knowledge. An analysis of covariance was performed on the quantitative data to test for significant differences between the means of the posttests for the control and experimental groups while controlling for pretest. It was found that there were no significant differences between the means of the control and experimental groups with respect to changes in their attitude toward science, anxiety toward teaching science and self efficacy toward teaching science. A significant difference between the means of the content examination was found (F(1,28) = 14.202 and p = 0.001), however, the result is questionable. The heat and energy module was the target for qualitative scrutiny. Coding for discrepant events was adapted from Appleton's 1996 work on student's responses to discrepant event science lessons. The following qualitative questions were posed for the investigation: (1) what were the ideas of the preservice elementary students prior to entering the classroom regarding heat and energy, (2) how effective were the discrepant events as presented in the PIiPS heat and energy module, and (3) how much does the "risk taking 2ff7e9595c
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