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Reconfigurable or programmable photonic devices are rapidly growing and have become an integral part of many optical systems. The ability to selectively modulate electromagnetic waves through electrical stimuli is crucial in the advancement of a variety of applications from data communication and computing devices to environmental science and space explorations. Chalcogenide‐based phase‐change materials (PCMs) are one of the most promising material candidates for reconfigurable photonics due to their large optical contrast between their different solid‐state structural phases. Although significant efforts have been devoted to accurate simulation of PCM‐based devices, in this paper, three important aspects which have often evaded prior models yet having significant impacts on the thermal and phase transition behavior of these devices are highlighted: the enthalpy of fusion, the heat capacity change upon glass transition, as well as the thermal conductivity of liquid‐phase PCMs. The important topic of switching energy scaling in PCM devices, which also helps explain why the three above‐mentioned effects have long been overlooked in electronic PCM memories but only become important in photonics, is further investigated. These findings offer insight to facilitate accurate modeling of PCM‐based photonic devices and can inform the development of more efficient reconfigurable optics. 

Geographically-based screening policies for diabetic retinopathy (DR) can be effective in developing teleretinal imaging (TRI) guidelines while identifying patients with limited geographic access to eye care. This study conducts cost-effectiveness analysis of different screening policies for urban and rural diabetic patients in Western Pennsylvania. A Monte Carlo simulation model was used to evaluate the cost-effectiveness of 2 standardized screening policies (annual clinic-based screening (ACS) and annual TRI-based screening (ATRI)) and a personalized TRI-based screening policy (PTRI) for both urban and rural cohorts. PTRI was generated by a previously developed mathematical model that autonomously makes semi-annual screening recommendations based on each patient’s disease progression and compliance (Dorali et al. IOVS 2022; 63(7)). For each policy, hypothetical urban and rural cohorts of 50,000 patients were simulated and lifetime QALYs and costs were collected for each patient. TRI compliance rates were derived from electronic medical records. Compliance with clinic-based screening was selected from literature-based values (12-45% for rural patients and 50-65% for urban patients). For a base case urban cohort with an A1C level of 7% and entering age of 40, costs per QALY gain (CPQ) for ACS, ATRI, and PTRI were $744.93±1.57, $792.38±1.64, and $714.60±1.56, respectively; PTRI produced more cost saving than ACS with the same QALY gain (See Fig 1). For a base case rural cohort, CPQ for ACS, ATRI, and PTRI were $869.15±1.80, $819.24±1.88, and $761.51±1.42, respectively; both ATRI and PTRI dominated ACS in QALY gains and cost saving (Fig 1). PTRI recommended TRI more to rural patients (94.13±0.01%) than to urban patients (87.20±0.02%). For the rural cohort, the minimum average TRI compliance rate such that ATRI is more cost-effective than ACS was 56% (Fig 2). TRI-based screening was found more beneficial for rural patients. PTRI was found dominant in QALY gain and cost saving for both urban and rural cohorts against standardized policies. These findings suggest that TRI is best utilized when location-specific factors such as geographic access to care or TRI compliance are considered. 

In radiation therapy treatment plan optimization, selecting a set of clinical objectives that are tractable and parsimonious yet effective is a challenging task. In clinical practice, this is typically done by trial and error based on the treatment planner’s subjective assessment, which often makes the planning process inefficient and inconsistent. We develop the objective selection problem that infers a sparse set of objectives for prostate cancer treatment planning based on historical treatment data. We formulate the problem as a nonconvex bilevel mixed-integer program using inverse optimization and highlight its connection with feature selection to propose multiple solution approaches, including greedy heuristics and regularized problems and application-specific methods that use anatomical information of the patients. Our results show that the proposed heuristics find objectives that are near optimal. Via curve analysis on dose-volume histograms, we show that the learned objectives closely represent latent clinical preferences. 

We present experimental studies of alternating current (AC) electrowetting dominantly influenced by several unique characteristics of an ion gel dielectric in its capacitance. At a high-frequency region above 1 kHz, the droplet undergoes the contact angle modification. Due to its high-capacitance characteristic, the ion gel allows the contact angle change as large as Δθ = 26.4°, more than 2-fold improvement, compared to conventional dielectrics when f = 1 kHz. At the frequency range from 1 to 15 kHz, the capacitive response of the gel layer dominates and results in a nominal variation in the angle change as θ ≈ 90.9°. Above 15 kHz, such a capacitive response of the gel layer sharply decreases and leads to the drastic increase in the contact angle. At a low-frequency region below a few hundred Hz, the droplet’s oscillation relying on the AC frequency applied was mainly observed and oscillation performance was maximized at corresponding resonance frequencies. With the high-capacitance feature, the ion gel significantly enlarges the oscillation performance by 73.8% at the 1st resonance mode. The study herein on the ion gel dielectric will help for various AC electrowetting applications with the benefits of mixing enhancement, large contact angle modification, and frequency-independent control.