Development and Validation of “Hazard O’Clock”: A Home Hazard and Disaster Awareness Game

The Philippines is the fourth most disaster-prone country in the world due to its location in the Pacific Ring of Fire and Pacific Typhoon Belt. When it comes to these disasters, children below the age of 18 are considered to be among the most vulnerable. This study aimed to develop a mobile game about Disaster Risk Reduction and Management (DRRM) in the home setting that can be used as a teaching aid for children. The information integrated into the game was from different resources made by various government agencies. The Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model was used in the development of the game, and game development educators and STEM educators evaluated it. Using a 5-point Likert scale survey, the game’s quality and appropriateness were evaluated for the following categories: Instructional Content, Functional Suitability, Performance Efficiency, and Usability. For each category, the mean score ratings were 4.43, 4.43, 4.80, and 4.60 respectively. Overall, the game received a rating of 4.52 indicating that it is Very Appropriate for its purpose. The research findings have shown that the game, Hazard O’Clock, could be used as a teaching aid for DRRM

Computational model of gastric motility with active-strain electromechanics

We present an electro-mechanical constitutive framework for the modeling of gastric motility, including pacemaker electrophysiology and smooth muscle contractility. In this framework, we adopt a phenomenological description of the gastric tissue. Tissue electrophysiology is represented by a set of two minimal two-variable models and tissue electromechanics by an active-strain finite elasticity approach. We numerically investigate the implication of the spatial distribution of pacemaker cells on the entrainment and synchronization of the slow waves characterizing gastric motility in health and disease. On simple schematic model geometries, we demonstrate that the proposed computational framework is amenable to large scale in-silico analyses of the complex gastric motility including the underlying electro-mechanical coupling

Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features

Abstract Small organic molecules can alter the degradation rates of the magnesium alloy ZE41. However, identifying suitable candidate compounds from the vast chemical space requires sophisticated tools. The information contained in only a few molecular descriptors derived from recursive feature elimination was previously shown to hold the potential for determining such candidates using deep neural networks. We evaluate the capability of these networks to generalise by blind testing them on 15 randomly selected, completely unseen compounds. We find that their generalisation ability is still somewhat limited, most likely due to the relatively small amount of available training data. However, we demonstrate that our approach is scalable; meaning deficiencies caused by data limitations can presumably be overcome as the data availability increases. Finally, we illustrate the influence and importance of well-chosen descriptors towards the predictive power of deep neural networks