Most of the colloidal clusters have been produced from oil-in-water emulsions with identical microspheres dispersed in oil droplets. Here, we present new types of binary colloidal clusters from phase-inverted water-in-oil emulsions using various combinations of two different colloids with several size ratios: monodisperse silica or polystyrene microspheres for larger particles and silica or titania nanoparticles for smaller particles. Obviously, a better understanding of how finite groups of different colloids self-organize in a confined geometry may help us control the structure of matter at multiple length scales. In addition, since aqueous dispersions have much better phase stability, we could produce much more diverse colloidal materials from water-in-oil emulsions rather than from oil-in-water emulsions. Interestingly, the configurations of the large microspheres were not changed by the presence of the small particles. However, the arrangement of the smaller particles was strongly dependent on the nature of the interparticle interactions. The experimentally observed structural evolutions were consistent with the numerical simulations calculated using Surface Evolver. These clusters with nonisotropic structures can be used as building blocks for novel colloidal structures with unusual properties or by themselves as light scatterers, diffusers, and complex adaptive matter exhibiting emergent behavior.

Given our aging society and the prevalence of age-related hearing loss that often develops during adulthood, hearing loss is a common public health issue affecting almost all older adults. Moderate-to-moderately severe hearing loss can usually be corrected with hearing aids; however, severe-to-profound hearing loss often requires a cochlear implant (CI). However, post-operative CI results vary, and the performance of the previous prediction models is limited, indicating that a new approach is needed. For postlingually deaf adults (n de120) who received CI with full insertion, we predicted CI outcomes using a Random-Forest Regression (RFR) model and investigated the effect of preoperative factors on CI outcomes. Postoperative word recognition scores (WRS) served as the dependent variable to predict. Predictors included duration of deafness (DoD), age at CI operation (ageCI), duration of hearing-aid use (DoHA), preoperative hearing threshold and sentence recognition score. Prediction accuracy was evaluated using mean absolute error (MAE) and Pearson’s correlation coefficient r between the true WRS and predicted WRS. The fitting using a linear model resulted in prediction of WRS with r = 0.7 and MAE = 15.6 ± 9. RFR outperformed the linear model (r = 0.96, MAE = 6.1 ± 4.7, p < 0.00001). Cross-hospital data validation showed reliable performance using RFR (r = 0.91, MAE = 9.6 ± 5.2). The contribution of DoD to prediction was the highest (MAE increase when omitted: 14.8), followed by ageCI (8.9) and DoHA (7.5). After CI, patients with DoD < 10 years presented better WRSs and smaller variations (p < 0.01) than those with longer DoD. Better WRS was also explained by younger age at CI and longer-term DoHA. Machine learning demonstrated a robust prediction performance for CI outcomes in postlingually deaf adults across different institutes, providing a reference value for counseling patients considering CI. Health care providers should be aware that the patients with severe-to-profound hearing loss who cannot have benefit from hearing aids need to proceed with CI as soon as possible and should continue using hearing aids until after CI operation.

Thermodynamic modeling of gasification process provides a quick estimate of performance of the gasifier. Most of the earlier work on thermodynamic modeling is restricted to a particular feedstockÀgasification agent combination and hence the results cannot be generalized. In the present work, the equilibrium modeling based on Gibb's free energy minimization approach is used to analyze the performance of gasification of any fuel using oxygen or steam. The performance is analyzed at the carbon boundary point at which the cold gas efficiency is maximum. The gasification temperature, amount of gasification agent required, composition of syngas, and cold gas efficiency are predicted using Aspen Plus. The results are presented as contour plots on Van Krevelen coordinates (H/C vs O/C) and interpreted based on simplified gasification reactions. The performance for different feedstocks represented in Van Krevelen diagram is also analyzed. Finally, advantage of cogasification of feedstocks is highlighted.

Ménière's Disease (MD) is difficult to diagnose and evaluate objectively over the course of treatment. Recently, several studies have reported MD diagnoses by MRI-based endolymphatic hydrops (EH) analysis. However, this method is time-consuming and complicated. Therefore, a fast, objective, and accurate evaluation tool is necessary. The purpose of this study was to develop an algorithm that can accurately analyze EH on intravenous (IV) gadolinium (Gd)-enhanced inner-ear MRI using artificial intelligence (AI) with deep learning. In this study, we developed a convolutional neural network (CNN)based deep-learning model named INHEARIT (INner ear Hydrops Estimation via ARtificial InTelligence) for the automatic segmentation of the cochlea and vestibule, and calculation of the EH ratio in the segmented region. Measurement of the EH ratio was performed manually by a neuro-otologist and neuro-radiologist and by estimation with the INHEARIT model and were highly consistent (intraclass correlation coefficient = 0.971). This is the first study to demonstrate that automated EH ratio measurements are possible, which is important in the current clinical context where the usefulness of IV-Gd inner-ear MRI for MD diagnosis is increasing. Ménière's disease (MD) is a multifactorial disorder with typical symptoms of recurrent spontaneous attacks of vertigo, fluctuating hearing loss, tinnitus, and sensations of ear fullness. Endolymphatic hydrops (EH) is a pathological finding where the endolymphatic spaces are distended by enlargements of endolymphatic volume, a histologic hallmark of MD 1-3. According to a 1995 consensus statement from the Committee on Hearing and Equilibrium of the American Association of Otolaryngology-Head and Neck Surgery (AAO-HNS), "certain" MD cases can only be confirmed by the histological demonstration of EH in postmortem temporal bone specimens 4. Therefore in 2015, a committee of the Bárány Society revised the diagnostic criteria to remove the concept of "certain MD" 5. Thus far, the diagnostic criteria have been changed due to the lack of tools to objectively find EH during life. However, with the advancement of imaging technology, MRI can be used to identify endolymphatic hydrops in MD patients as an objective marker. In 2004, Duan et al. succeeded in visualizing EH in vivo for the first time in a guinea pig using 4.7 T MRI 6. Nakashima et al. succeeded in confirming EH after injecting contrast media through intratympanic (IT) and intravenous (IV) injections into MD patients using 3 T MRI 7,8. Recently, many reports have been published regarding the use of MRI to assess EH. In particular, IV gadolinium (Gd)-enhanced inner-ear MRI has shown good results 9,10. We have also proven through previous studies that IV-Gd inner-ear MRI is very useful for diagnosing MD by demonstrating the correlation of hydrops with