Knoxpetersen0953

Results are shown on simulated and experimental data collected on a saline-filled tank with agar targets simulating the conductivity of the heart and lungs.

The results demonstrate that deep neural networks can successfully learn the mapping between scattering transforms and the internal boundaries of structures.

The results demonstrate that deep neural networks can successfully learn the mapping between scattering transforms and the internal boundaries of structures.

Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and wider deployment. An informative next step towards this goal would be a new method to obtain ground kinetics in the field.

Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived sturrence of non-contact injuries in elite and community-level sports.

Coaching, medical, and allied health staff could ultimately use this technology to monitor a range of joint loading indicators during game play, with the aim to minimize the occurrence of non-contact injuries in elite and community-level sports.

Hepatocellular carcinoma (HCC) is one of the most dangerous, and fatal cancers. Thermal ablation proved its power as the best treatment method for HCC. In microwave thermal ablation, microwave probes are used to generate electromagnetic waves (EMW) at microwave (MW) frequency 2.45GHz. In this paper, the design/model of a novel microwave ablation probe, namely a single slot with a shifted 1T-ring probe is presented for HCC therapy.

A Finite Element Method (FEM) is employed to model the probe and the hepatic tumor liver tissues. The relation between the tip of probe position and the center of the hepatic tumor was studied to determine the best probe location at which a minimum MW power is required to ablate the entire tumor tissues with the smallest damage in the nearby healthy tissues to the tumor.

The results indicated that the ablated part of the tissues varies depending on the MW probe type, the amount of used power, the location of the probe, and the exposure time. Hepatic tumors' diameters from 2-5cm were studied.

It was shown that the proposed SSS 1T-ring (single slot with shifted 1T-ring) probe provided the best ablation performance when the probe's tip placed below the tumor's center by 11 mm, which achieved 100% damage in the tumor tissues using 6 W power for 10 minutes.

When the probe's tip is located at the center of the tumor, the ablation rate was 73.45% in the tumor tissues under the same conditions.

When the probe's tip is located at the center of the tumor, the ablation rate was 73.45% in the tumor tissues under the same conditions.Medicated chewing gum has been recognised as a new advanced drug delivery method, with a promising future. Its potential has not yet been fully exploited because currently there is no gold standard for testing the release of agents from chewing gum in vitro. This study presents a novel humanoid chewing robot capable of closely replicating the human chewing motion in a closed environment, incorporating artificial saliva and allowing measurement of xylitol release from the gum. The release of xylitol from commercially available chewing gum was quantified following both in vitro and in vivo mastication. The chewing robot demonstrated a similar release rate of xylitol as human participants. The greatest release of xylitol occurred during the first 5 minutes of chewing and after 20 minutes of chewing only a low amount of xylitol remained in the gum bolus, irrespective of the chewing method used. selleck Saliva and artificial saliva solutions respectively were collected after 5, 10, 15 and 20 minutes of continuous chewing and the amount of xylitol released from the chewing gum determined. Bioengineering has been implemented as the key engineering strategy to create an artificial oral environment that closely mimics that found in vivo. These results demonstrate the chewing robot with built-in humanoid jaws could provide opportunities for pharmaceutical companies to investigate and refine drug release from gum, with reduced patient exposure and reduced costs using this novel methodology.

In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements.

Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements.

Upon termination of the 20 weeks long in-silico trial, in a scenario characterized by meal carbohydrate (CHO) normally distributed with mean μ = [50, 75, 75] grams and standard deviation σ = [5, 7, 7] grams, our strategy produced statistically significant improvements in time in range (70--180) [mg/dl], from 66.9(33.1) % to 93.6(6.7) %, p = 0.02.

Iterative learning shows potential to improve glycemic regulation over time by driving blood glucose closer to the recommended glycemic targets.

Decision support systems (DSSs) and automated therapy advisors such as the one proposed here are expected to improve glycemic outcomes reducing the burden on patients on MDI therapy.

Decision support systems (DSSs) and automated therapy advisors such as the one proposed here are expected to improve glycemic outcomes reducing the burden on patients on MDI therapy.