Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Control theory & Artificial neural network. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Fabrication of 13-93 bioactive glass scaffolds for bone tissue engineering using indirect selective laser sintering

Abstract: Bioactive glasses are promising materials for bone scaffolds due to their ability to assist in tissue regeneration. When implanted in vivo, bioactive glasses can convert into hydroxyapatite, the main mineral constituent of human bone, and form a strong bond with the surrounding tissues, thus providing an advantage over polymer scaffold materials. Bone scaffold fabrication using additive manufacturing techniques can provide control over pore interconnectivity during fabrication of the scaffold, which helps in mimicking human trabecular bone. 13-93 glass, a third-generation bioactive material designed to accelerate the body's natural ability to heal itself, was used in the research described herein to fabricate bone scaffolds using the selective laser sintering (SLS) process. 13-93 glass mixed with stearic acid (as the polymer binder) by ball milling was used as the powder feedstock for the SLS machine. The fabricated green scaffolds underwent binder burnout to remove the stearic acid binder and were then sintered at temperatures between 675 °C and 695 °C. The sintered scaffolds had pore sizes ranging from 300 to 800 µm with 50% apparent porosity and an average compressive strength of 20.4 MPa, which is excellent for non-load bearing applications and among the highest reported for an interconnected porous scaffold fabricated with bioactive glasses using the SLS process. The MTT labeling experiment and measurements of MTT formazan formation are evidence that the rough surface of SLS scaffolds provides a cell-friendly surface capable of supporting robust cell growth.

Discrete-Time Ripple Correlation Control for Maximum Power Point Tracking

Abstract: Ripple correlation control (RCC) is a high-performance real-time optimization technique that has been applied to photovoltaic maximum power point tracking. This paper extends the previous analog technique to the digital domain. The proposed digital implementation is less expensive, more flexible, and more robust. With a few simplifications, the RCC method is reduced to a sampling problem; that is, if the appropriate variables are sampled at the correct times, the discrete-time RCC (DRCC) algorithm can quickly find the optimal operating point. First, the general DRCC method is derived and stability is proven. Then, DRCC is applied to the photovoltaic maximum power point tracking problem. Experimental results verify tracking accuracy greater than 98% with an update rate greater than 1 kHz.

Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images

Abstract: This paper presents an approach that combines conventional image processing with deep learning by fusing the features from the individual techniques. We hypothesize that the two techniques, with different error profiles, are synergistic. The conventional image processing arm uses three handcrafted biologically inspired image processing modules and one clinical information module. The image processing modules detect lesion features comparable to clinical dermoscopy information—atypical pigment network, color distribution, and blood vessels. The clinical module includes information submitted to the pathologist—patient age, gender, lesion location, size, and patient history. The deep learning arm utilizes knowledge transfer via a ResNet-50 network that is repurposed to predict the probability of melanoma classification. The classification scores of each individual module from both processing arms are then ensembled utilizing logistic regression to predict an overall melanoma probability. Using cross-validated results of melanoma classification measured by area under the receiver operator characteristic curve (AUC), classification accuracy of 0.94 was obtained for the fusion technique. In comparison, the ResNet-50 deep learning based classifier alone yields an AUC of 0.87 and conventional image processing based classifier yields an AUC of 0.90. Further study of fusion of conventional image processing techniques and deep learning is warranted.