Fabrication of Microfiber Patterns with Ivy Shoot-Like Geometries Using Improved Electrospinning

Fibers and fibrous structures are used extensively in various fields due to their many advantages. Microfibers, as well as nanofibers, are considered to be some of the most valuable forms of advanced materials. Accordingly, various methods for fabricating microfibers have been developed. Electrospinning is a useful fabrication method for continuous polymeric nano- and microfibers with attractive merits. However, this technique has limitations in its ability to control the geometry of fibrous structures. Herein, advanced electrospinning with direct-writing functionality was used to fabricate microfiber patterns with ivy shoot-like geometries after experimentally investigating the effects of the process conditions on the fiber formation. The surface properties of the fibers were also modified by introducing nanoscale pores through the use of higher levels of humidity during the fabrication process.ope

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

We study contextual linear bandit problems under uncertainty on features; they are noisy with missing entries. To address the challenges from the noise, we analyze Bayesian oracles given observed noisy features. Our Bayesian analysis finds that the optimal hypothesis can be far from the underlying realizability function, depending on noise characteristics, which is highly non-intuitive and does not occur for classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. We thus propose an algorithm aiming at the Bayesian oracle from observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page

Visual Function after Primary Posterior Chamber Intraocular Lens Implantation in Pediatric Unilateral Cataract: Stereopsis and Visual Acuity

PURPOSE: To investigate the association between binocular function and vision after cataract removal and primary posterior chamber intraocular lens (PC-IOL) implantation in children with unilateral cataract and to identify visual function differences according cataract type. METHODS: Clinical records of 2- to 6-year-old patients with unilateral cataract removal and primary PC-IOL implantation were reviewed retrospectively. Visual acuity and ocular alignment were measured. Sensory fusion was assessed with the Worth 4-dot test, and stereoacuity with the Titmus stereo test. Cataracts were classified according to cause, lens opacity location, age at onset, and presence of strabismus. Clinical characteristics of patients who obtained good visual function were identified. RESULTS: Forty-seven patients were included. Among 22 (46.8%) with good vision (20/40 or better), only 6 (27.3%) achieved good binocular function (the presence of fusion and 100 seconds of arc or better of stereoacuity). Visual acuity was better in eyes with good binocular function (p=0.002). No other variables were significant for achieving good binocular function. CONCLUSIONS: The removal of unilateral cataract in a visually immature child can result in a combination of good visual acuity and binocular function. Good binocular function is closely related to good visual acuity

Prediction of males’ physical work capacity in various simulated altitudes using an incremental cycle ergometer exercise test at sea level

Standard approach to predict the decrease in physical fitness that will occur following a transition to a higher altitude is unavailable. Therefore, the study aimed to design simple mathematical models to predict submaximal exercise performance in various altitude environments, using a simple physical work capacity test conducted at sea level involving >200 subjects. After splitting the subjects’ data in a ratio of 7:3, we used 70% of the data for regression model development and employed 30% for cross-validation testing. All subjects performed submaximal exercise tests using a cycle ergometer at artificial altitudes of 2000 m, 3000 m, 4000 m, 5000 m, and at sea level. We applied simple regression analysis to create a predictive model with the statistical significance set at the level of <5%. There were 233 subjects involved in this study. The coefficient of determination of our regression model was 40–58%, and the standard error of estimation was 14.96–17.27 watts. The cross-validation of our regression model was 8–10%. Among the regression models developed, the one applied to an artificial altitude of 5000 m was 17%, and the regression model applied to an artificial altitude below 4000 m had no issues in generalization since the cross-validation was less than 10%. However, the regression model applied to an artificial altitude of 5000 m had a cross-validity of 17%; therefore, it should be used with caution