Vowels of Beryozovka Ewen: An Acoustic Phonetic Study

This study acoustically analyzes 13,540 vowel tokens of Beryozovka Ewen with the aid of automated post-transcriptional processing technique. The focus of the analysis is on the acoustic correlates of [RTR], which is the harmonic feature of the language. In addition to the first three formants, acoustic values representing spectral tilt such as H1−H2, H1−A2, and B1 are measured as potential acoustic cues of [RTR]. The results show that F1, F3, and B1 are the most reliable cues of the feature and that H1− H2 and H1−A2 are nearly reliable. These acoustic cues are also shown to interact with length and position. In general, the acoustic distance between [−RTR] and [+RTR] vowels are farther in long and word-initial vowels than in short and non-initial vowels, respectively. We claim that [RTR] is more appropriate for the harmonic feature of Ewen than [ATR], and that the greater perceptibility of word-initial vowels is understood as a means to facilitate the lexical access in a language with vowel harmony

A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis

An eye tracking based framework for safety improvement of offshore operations

Offshore drilling operations consist of complex and high-risk processes. Lack of situational awareness in drilling operations has become an important human factor issue that causes safety accidents. Prolonged work shifts and fatigue are some of the crucial issues that impact performance. Eye tracking technology can be used to distinguish the degree of awareness or alertness of participants that might be related to fatigue or onsite distractions. Oculomotor activity can be used to obtain visual cues that can quantify the drilling operators’ situational awareness that might enable us to develop warning alarms to alert the driller. Such systems can help reduce accidents and save non-productive time. In this paper, eye movement characteristics were investigated to differentiate the situational awareness between a representative expert and a group of novices using a scenario-based Virtual Reality Drilling Simulator. Significant visual oculomotor activity differences were identified between the expert and the novices that indicate an eye-tracking based system can detect the distraction and alertness exhibited by the workers. Results show promise on developing a framework which implements a real-time eye tracking technology in various drilling operations at drilling rigs and Real Time Operation Centers to improve process safety

The Role of Confined Water in Ionic Liquid Electrolytes for Dye-Sensitized Solar Cells

Ionic liquids (ILs) provide an attractive medium for various chemical and redox reactions, where they are generally regarded as hydrophobic. However, Seddon et al. discovered that 4–10 wt % water absorbs into ILs that contain bulky anions, and Cammarata et al. found that the molecular state of water in ILs is dramatically different from that of bulk liquid water or that of water vapor. To determine the microstructure of water incorporated into ILs and the impact on properties, we carried out first-principles-based molecular dynamics simulations. We find water in three distinct phases depending on water content, and that the transport properties depend on the nature of the water phases. These results suggest that the optimal water content is ~10% mole fraction of water molecules (~1.1 wt %) for applications such as nonvolatile electrolytes for dye-sensitized solar cells (DSSCs). This suggests a strategy for improving the performance of IL DSSC by replacing water with additives that would play the same role as water (since too much water can deteriorate performance at the anode–dye interface)