Converting capsules to sensors for nondestructive analysis:from cargo-responsive self-sensing to functional characterization

A general concept of converting capsules into sensors is reported. Such simple conversion enables instantaneous nondestructive analysis for applications such as controlled release and energy storage among others. Converted capsule sensors are responsive in emission colors to varying core cargos via the incorporation of a solvatochromic fluorophore under excitation. Such cargo-responsive self-sensing abilities facilitate their application in capsule-level analysis such as cargo retention-leakage detection and release implications, as well as defect identification. The versatile concept is shown as an auxiliary tool in thermal energy storage to visualize phase transition, exhibiting promising potentials in application-level characterization

Comparison of perceptual learning of real and virtual line orientations: An event-related potential study

AbstractWhen investigating perceptual learning (PL), most researchers use real figures as stimuli, but PL can occur when subjects are trained with virtual stimuli or even without any visual stimuli at all. Here, we first demonstrated that virtual lines have the same perceptual attributes as real lines by confirming that there is also an oblique effect in virtual lines (formed by a pair of circles) in an orientation discrimination task. Then, our ERP study showed that orientation discrimination learning and its transfer across real and virtual lines were associated with more negative parietal–occipital P1–N1 (reduced P1 and enhanced N1), which indicated the involvement of high-level stages of visual information processing or the involvement of top-down influences. At the same time, the specific ERP changes in the frontal ERP components were differently associated with real versus virtual line orientation learning. That is, real line learning was characterized by an early and short-lasting frontal N1 (120–140ms) reduction, in contrast to a much later, widespread, and long-lasting P150–300 decrease in virtual line learning. These results contribute to the understanding of the neural basis of perceptual learning and the distinction between real and virtual stimulus learning

Robust Tube Model Predictive Control with Uncertainty Quantification for Discrete-Time Linear Systems

This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and hard constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an MPC algorithm incorporating online uncertainty quantification that builds on prior knowledge of the disturbance, i.e., a known but conservative disturbance set. We approximate the true disturbance set at each time step with a parameterised set, which is referred to as a quantified disturbance set, using the scenario approach with additional disturbance realisations collected online. A key novelty of this paper is that the parameterisation of these quantified disturbance sets enjoy desirable properties such that the quantified disturbance set and its corresponding rigid tube bounding disturbance propagation can be efficiently updated online. We provide statistical gaps between the true and quantified disturbance sets, based on which, probabilistic recursive feasibility of MPC optimisation problems are discussed. Numerical simulations are provided to demonstrate the efficacy of our proposed algorithm and compare with conventional robust MPC algorithms.Comment: 8 page

Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning

Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety

Online dosimetric evaluation of larynx SBRT: A pilot study to assess the necessity of adaptive replanning

PURPOSE: We have initiated a multi-institutional phase I trial of 5-fraction stereotactic body radiotherapy (SBRT) for Stage III-IVa laryngeal cancer. We conducted this pilot dosimetric study to confirm potential utility of online adaptive replanning to preserve treatment quality. METHODS: We evaluated ten cases: five patients enrolled onto the current trial and five patients enrolled onto a separate phase I SBRT trial for early-stage glottic larynx cancer. Baseline SBRT treatment plans were generated per protocol. Daily cone-beam CT (CBCT) or diagnostic CT images were acquired prior to each treatment fraction. Simulation CT images and target volumes were deformably registered to daily volumetric images, the original SBRT plan was copied to the deformed images and contours, delivered dose distributions were re-calculated on the deformed CT images. All of these were performed on a commercial treatment planning system. In-house software was developed to propagate the delivered dose distribution back to reference CT images using the deformation information exported from the treatment planning system. Dosimetric differences were evaluated via dose-volume histograms. RESULTS: We could evaluate dose within 10 minutes in all cases. Prescribed coverage to gross tumor volume (GTV) and clinical target volume (CTV) was uniformly preserved; however, intended prescription dose coverage of planning treatment volume (PTV) was lost in 53% of daily treatments (mean: 93.9%, range: 83.9-97.9%). Maximum bystander point dose limits to arytenoids, parotids, and spinal cord remained respected in all cases, although variances in carotid artery doses were observed in a minority of cases. CONCLUSIONS: Although GTV and CTV SBRT dose coverage is preserved with in-room three-dimensional image guidance, PTV coverage can vary significantly from intended plans and dose to critical structures may exceed tolerances. Online adaptive treatment re-planning is potentially necessary and clinically applicable to fully preserve treatment quality. Confirmatory trial accrual and analysis remains ongoing