A Study on Virtual Reality Storytelling by Story Authoring Tool Algorithm

The objective of this study was to examine the storytelling principles of virtual reality contents, which are recently grabbing much attention, and the patterns of their generation rules and, based on the results, to analyze the elements and structure of a storytelling method suitable for virtual reality contents. In virtual reality environment, a story is usually being generated between choices made by a user who behaves autonomously under simulated environmental factors and the environmental constraints. This corresponds to a mutually complementary role of representation and simulation, which has been hotly discussed in the field of interactive storytelling. This study was conducted based on the assumption that such a mutually complementary realization is ideal for virtual reality storytelling. A simulation-based story authoring tool is a good example that shows this mutual complementation, in that it develops a story through various algorithms which involves the interaction of agents which occur within the strata of a virtual environment. Therefore, it can be a methodology for virtual reality storytelling. The structures and elements of narratives used in virtual reality storytelling which achieve balance of representation and simulation are much similar to an algorithm strategy of a simulation-based story authoring tool. The virtual reality contents released up to now can be classified into four categories based on the two axes of representation and simulation. The study focused on contents which are layered in higher strata of both representation and simulation. In the perspective of representation strata, these contents are actively using such elements as goal, event, action, perception, internal element, outcome, and setting element, which are constituents of ‘Fabula model’, to generate time relations and cause-effect relations. And in the perspective of simulation strata, the use of the ‘Late commitment’ strategy allowed users to understand the meanings of their actions taken during the process of experimenting with various dynamic principles within the environment

Quantitative Analysis of Lens Nuclear Density Using Optical Coherence Tomography (OCT) with a Liquid Optics Interface: Correlation between OCT Images and LOCS III Grading

Purpose. To quantify whole lens and nuclear lens densities using anterior-segment optical coherence tomography (OCT) with a liquid optics interface and evaluate their correlation with Lens Opacities Classification System III (LOCS III) lens grading and corrected distance visual acuity (BCVA). Methods. OCT images of the whole lens and lens nucleus of eyes with age-related nuclear cataract were analyzed using ImageJ software. The lens grade and nuclear density were represented in pixel intensity units (PIU) and correlations between PIU, BCVA, and LOCS III were assessed. Results. Forty-seven eyes were analyzed. The mean whole lens and lens nuclear densities were 26.99 ± 5.23 and 19.43 ± 6.15 PIU, respectively. A positive linear correlation was observed between lens opacities (R2 = 0.187, p<0.01) and nuclear density (R2 = 0.316, p<0.01) obtained from OCT images and LOCS III. Preoperative BCVA and LOCS III were also positively correlated (R2 = 0.454, p<0.01). Conclusions. Whole lens and lens nuclear densities obtained from OCT correlated with LOCS III. Nuclear density showed a higher positive correlation with LOCS III than whole lens density. OCT with a liquid optics interface is a potential quantitative method for lens grading and can aid in monitoring and managing age-related cataracts

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Jamming transition in a highly dense granular system under vertical vibration

The dynamics of the jamming transition in a three-dimensional granular system under vertical vibration is studied using diffusing-wave spectroscopy. When the maximum acceleration of the external vibration is large, the granular system behaves like a fluid, with the dynamic correlation function G(t) relaxing rapidly. As the acceleration of vibration approaches the gravitational acceleration g, the relaxation of G(t) slows down dramatically, and eventually stops. Thus the system undergoes a phase transition and behaves like a solid. Near the transition point, we find that the structural relaxation shows a stretched exponential behavior. This behavior is analogous to the behavior of supercooled liquids close to the glass transition.Comment: 5 pages, 5 figures, accepted by Phys. Rev.

A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu