Optimal Multi-Interface Selection for Mobile Video Streaming in Efficient Battery Consumption and Data Usage

With the proliferation of high-performance, large-screen mobile devices, users’ expectations of having access to high-resolution video content in smooth network environments are steadily growing. To guarantee such stable streaming, a high cellular network bandwidth is required; yet network providers often charge high prices for even limited data plans. Moreover, the costs of smoothly streaming high-resolution videos are not merely monetary; the device’s battery life must also be accounted for. To resolve these problems, we design an optimal multi-interface selection system for streaming video over HTTP/TCP. An optimization problem including battery life and LTE data constraints is derived and then solved using binary integer programming. Additionally, the system is designed with an adoption of split-layer scalable video coding, which provides direct adaptations of video quality and prevents out-of-order packet delivery problems. The proposed system is evaluated using a prototype application in a real, iOS-based device as well as through experiments conducted in heterogeneous mobile scenarios. Results show that the system not only guarantees the highest-possible video quality, but also prevents reckless consumption of LTE data and battery life

Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics

Functional magnetic resonance imaging (fMRI) with optogenetic neural manipulation is a powerful tool that enables brain-wide mapping of effective functional networks. To achieve flexible manipulation of neural excitation throughout the mouse cortex, we incorporated spatiotemporal programmable optogenetic stimuli generated by a digital micromirror device into an MRI scanner via an optical fiber bundle. This approach offered versatility in space and time in planning the photostimulation pattern, combined with in situ optical imaging and cell-type-specific or circuit-specific genetic targeting in individual mice. Brain-wide effective connectivity obtained by fMRI with optogenetic stimulation of atlas-based cortical regions is generally congruent with anatomically defined axonal tracing data but is affected by the types of anesthetics that act selectively on specific connections. fMRI combined with flexible optogenetics opens a new path to investigate dynamic changes in functional brain states in the same animal through high-throughput brain-wide effective connectivity mapping. © 2023 Elsevier Inc.11Nsciescopu

Modular Fabrication of Hybrid Bulk Heterojunction Solar Cells Based on Breakwater-like CdSe Tetrapod Nanocrystal Network Infused with P3HT

We demonstrate the modular fabrication of nanocrystal/polymer hybrid bulk heterojunction solar cells based on breakwater-like CdSe tetrapod (TP) nanocrystal networks infused with poly­(3-hexylthiophene) (P3HT). This fabrication method consists of sequential steps for forming the hybrid active layers: the assembly of a breakwater-like CdSe TP network followed by nanocrystal surface modification and the infusion of semiconducting polymers. Such a modular approach enables the independent control of the nanoscopic morphology and surface chemistry of the nanocrystals, which are generally known to exhibit complex correlations, in a reproducible manner. Using these devices, the influence of the passivation ligands on solar cell characteristics could be clarified from temperature-dependent solar cell experiments. We found that a 2-fold increase in the short-circuit current with 1-hexylamine ligands, compared with the value based on pyridine ligands, originates from the reduced depth of trap states, minimizing the trap-assisted bimolecular recombination process. Overall, the work presented herein provides a versatile approach to fabricating nanocrystal/polymer hybrid solar cells and systematically analyzing the complex nature of these devices

Interobserver Variability Prediction of Primary Gross Tumor in a Patient with Non-Small Cell Lung Cancer

This research addresses the problem of interobserver variability (IOV), in which different oncologists manually delineate varying primary gross tumor volume (pGTV) contours, adding risk to targeted radiation treatments. Thus, a method of IOV reduction is urgently needed. Hypothesizing that the radiation oncologist’s IOV may shrink with the aid of IOV maps, we propose IOV prediction network (IOV-Net), a deep-learning model that uses the fuzzy membership function to produce high-quality maps based on computed tomography (CT) images. To test the prediction accuracy, a ground-truth pGTV IOV map was created using the manual contour delineations of radiation therapy structures provided by five expert oncologists. Then, we tasked IOV-Net with producing a map of its own. The mean squared error (prediction vs. ground truth) and its standard deviation were 0.0038 and 0.0005, respectively. To test the clinical feasibility of our method, CT images were divided into two groups, and oncologists from our institution created manual contours with and without IOV map guidance. The Dice similarity coefficient and Jaccard index increased by ~6 and 7%, respectively, and the Hausdorff distance decreased by 2.5 mm, indicating a statistically significant IOV reduction (p < 0.05). Hence, IOV-net and its resultant IOV maps have the potential to improve radiation therapy efficacy worldwide