Forecasting electricity consumption using SARIMA method in IBM SPSS software

Forecasting is a prediction of future values based on historical data. It can be conducted using various methods such as statistical methods or machine learning techniques. Electricity is a necessity of modern life. Hence, accurate forecasting of electricity demand is important. Overestimation will cause a waste of energy but underestimation leads to higher operation costs. Univesity Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian technical university, therefore there is a need to forecast UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. The monthly UTHM electricity consumption data exhibits seasonality-periodic fluctuations. Thus, the seasonal Autoregressive Integrated Moving Average (SARIMA) method was applied in IBM SPSS software to predict UTHM electricity consumption for 2019 via Box-Jenkins method and Expert Modeler. There were a total of 120 observations taken from January year 2009 to December year 2018 to build the models. The best model from both methods is SARIMA(0, 1, 1)(0, 1, 1)12. It was found that the result through the Box-Jenkins method is approximately the same with the result generated through Expert Modeler in SPSS with MAPE of 8.4%

Analysis of changing risk factors and explanation of risk predictions with machine learning for improved hamstring strain injury prevention in Australian football

Professional athletes and organizations can face significant consequences as a result of injury incidents in sports. Therefore, an abundance of studies has been conducted to identify the risk factors in the hope of preventing injuries from occurring in the first place. Hamstring strain injuries (HSIs) are the most frequent injuries in Australian Football League (AFL). Many studies had shown that there are several prominent risk factors for HSIs. However, this finding cannot be identified with any consistency through assessing the risk factors at a single time point, typically the beginning of a season (e.g., in the pre-season) or more frequently throughout the season (e.g., in the pre-season, early in-season and late in-season). Nonetheless, these studies did not consider the potential variability of risk factors across the season. In light of this, it was hypothesised that risk factors may vary depending on the time of the season. This thesis aims to answer if the risk of hamstring strain injuries in Australian Football can be reduced through a better understanding of the changing risk factors over the course of the season. Despite the study, identifying HSI risk at individual-level remains a challenge. This study aims to explore whether the risk of HSI for individual players can be better understood by explaining the predictions of machine learning (ML) models. The study utilised recursive feature selection and cross-validation to provide a holistic understanding of important risk factors at different points. Subsequently, counterfactual explanations were effectively generated for players at risk of sustaining HSI. The study found that non-modifiable risk factors were primarily linked to pre-season injuries, whereas modifiable risk factors were mostly associated with early in-season injuries. Counterfactual explanations and ML models offer a novel perspective in interpreting risk and finding potential solutions. Overall, this study provides new insights into risk factors associated with HSIs at different time points, as well as offers a solution for interpreting risk at individual-level using ML models and counterfactual explanations. The findings have important implications for researchers and practitioners who seek to mitigate the risk of HSI in the future

Computations of Multiphase Fluid Flows Using Marker-Based Adaptive, Multilevel Cartesian Grid Method

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76194/1/AIAA-2007-336-338.pd

Finite element analysis (FEA) modeling on adhesive joint for composite fuselage model

In this paper, a finite element modeling via ABAQUS/Explicit simulation on a novel fabrication miniature composite fuselage structure is presented. The fuselage structure is modeled as a continuum composite layup that consisted of a woven C-glass fiber/epoxy 200 g/m 2 composite laminated [90 8] with the orthotropic elastic material properties and adhesively bonded butt joint. The adhesively bonded joint progression is modeled using cohesive elements technology. For the purpose of FEA modeling, an experiment of double cantilever beam (DCB) according to ASTM standard D5528 is performed to determine the adhesive mode-I critical toughness. The mode-I interlaminar fracture toughness data (G I) are calculated and compared by four different methods according to the ASTM standard: BT, beam theory, MBT, modified beam theory, CC, compliance calibration method and MCC, modified compliance calibration method. The results indicate that ABAQUS/Explicit is able to reproduce satisfactory adhesive joint behavior using cohesive elements and collapse modes under crushing process

3-D Multiscale Adaptive Eulerian-Lagrangian Method for Multiphase Flows with Phase Change

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83641/1/AIAA-2010-1295-103.pd

A Unified Adaptive Cartesian Grid Method for Solid-Multiphase Fluid Dynamics with Moving Boundaries

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76397/1/AIAA-2007-4576-676.pd