The use of simulation as a teaching methodology in medical institutions has been in Malaysia for over two decades. This study aimed to evaluate the current scenarios of simulation impact and utilization in Malaysian academic healthcare institutions (AHIs). We conducted a population-based survey on all AHIs in Malaysia including public and private. We performed an online survey followed by a face-to-face interview evaluating the number of institutions that used simulation, duration of experience, purpose, funding, users’ category and healthcare domain, research activities, dedicated-trained staff and the challenges faced. Out of 75 healthcare institutions approached, 38 agreed to participate in this study. Twenty-two (57.9%) were public hospitals while 16 (42.1%) were private institutions. Thirty-five (92.1%) out of 38 institutions used simulation as a teaching method. The majority (15, 42.9%) had less than five years’ experience, and about a third (11, 31.4%) used simulation for teaching, training and performance assessment. Nurses (30, 26.1%) were the main users followed by physicians and paramedic (19, 16.5% each respectively). In-hospital and procedural group were the top two domains of utilizers. Almost three quarters (25, 71.4%) have dedicated support staff to manage the centre. Funding was mainly from internal institutional support mechanisms. Seven different categories of challenges were identified, the biggest being financial support. In summary, even though healthcare simulation has been in Malaysia for the past two decades but the most substantial impact happened over the last five years. Utilization was mainly for teaching, training, and performance assessment with minimal use in research.
Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.