Craniofacial Phenotype in Obstructive Sleep Apnea and Its Impact on Positive Airway Pressure (PAP) Adherence.

Positive airway pressure (PAP) is an important treatment tool for patients with moderate and severe obstructive sleep apnea (OSA), and adherence to PAP significantly affects treatment outcomes. Disease severity, adverse effects, and psychosocial factors are known to predict medication adherence. Cephalometric parameters have been reported to positively correlate with upper airway collapse. However, research on the correlation between these cephalometric parameters and PAP adherence remains insufficient. This study aimed to identify this relationship. This study included 185 patients with OSA who were prescribed PAP. Polysomnography (PSG) was performed to diagnose OSA, and paranasal sinus computed tomography (PNS CT) was performed to check for comorbidities of the upper airway. In addition, cephalometric parameters such as the hyoid-posterior nasal spine (H-PNS), posterior nasal spine-mandibular plane (PNS-MP), and hyoid-mandibular plane (H-MP) were measured in the midsagittal and axial CT views. Adherence was evaluated 3-12 months after the PAP prescription. A total of 136 patients were PAP-adherent, and 49 were nonadherent. There were more males in the adherent group and a higher average height in the adherent group. The PSG results showed that the apnea-hypopnea index (AHI), respiratory disturbance index (RDI), oxygen desaturation index (ODI), arousal index (AI), rapid eye movement (REM) AHI, and supine AHI were significantly higher, and the lowest oxygen saturation was lower in the adherent group. In the analysis of covariance (ANCOVA) model adjusted for sex and height, among the cephalometric parameters, H-MP was significantly longer in the adherent group (p = 0.027), and H-PNS showed a longer tendency (p = 0.074). In the logistic regression analysis model, the odds ratio (OR) and 95% confidence intervals (95% CI) of adherence and severe OSA in the third tertile compared to the first tertile of H-MP were 2.93 (1.25-6.86) and 4.00 (1.87-8.56). In the case of H-PNS, they were 2.58 (1.14-5.81) and 4.86 (2.24-10.54), respectively. This study concluded that an inferiorly placed hyoid bone in adult patients is associated with PAP adherence and disease severity.

A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters.

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.

A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems.

Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster prediction due to the impracticability of defining high-fidelity physics-based models for complex natural disaster behavior and the dependency of data-driven models on the training dataset. This causes disaster management systems to inappropriately use disaster response resources, including medical personnel, rescue equipment and relief supplies, to ensure that it may increase the damages from the natural disasters. This study proposes a digital twin architecture to provide accurate disaster prediction services with a similarity-based hybrid modeling scheme. The hybrid modeling scheme creates a hybrid disaster model that compensates for the errors of physics-based prediction results with a data-driven error correction model to enhance the prediction accuracy. The similarity-based hybrid modeling scheme reduces errors from the data dependency of the hybrid model by constructing a training dataset using similarity assessments between the target disaster and the historical disasters. Evaluations in wildfire scenarios show that the digital twin decreases prediction errors by approximately 50% compared with those of the existing schemes.

Low-power motion gesture sensor with a partially open cavity package.

In an IR proximity-based motion gesture sensor (MGS) mainly composed of an IR LED and photodiodes (PDs), the IR LED is the most power-hungry component. For reducing its power, both the size and the field-of-view (FOV) of each PD can be increased instead. However, it cannot be adapted to conventional MGSs due to the geometrical limitation of their optical structures. In this paper, the optical structure of the proposed MGS with a partially open cavity package allows for power reduction by at least 70.9% compared with conventional MGSs. Optical simulation and test results validate the theoretical analysis presented.