Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

Noble Metal Nanoparticles Incorporated Siliceous TUD-1 Mesoporous Nano-Catalyst for Low-Temperature Oxidation of Carbon Monoxide

Abstract: This report, for the first time, demonstrated the low-temperature oxidation of carbon monoxide (CO) using nano-catalysts consisting of noble metal nanoparticles incorporated in TUD-1 mesoporous silica nano-structures synthesized via a one-pot surfactant-free sol–gel synthesis methodology. Herein, we investigated a nano-catalyst, represented as M-TUD-1 (M = Rh, Pd, Pt and Au), which was prepared using a constant Si/M ratio of 100. The outcome of the analytical studies confirmed the formation of a nano-catalyst ranging from 5 to 10 nm wherein noble metal nanoparticles were distributed uniformly onto the mesopores of TUD-1. The catalytic performance of M-TUD-1 catalysts was examined in the environmentally impacted CO oxidation reaction to CO2. The catalytic performance of Au-TUD-1 benchmarked other M-TUD-1 catalysts and a total conversion of CO was obtained at 303 K. The activity of the other nano-catalysts was obtained as Pt-TUD-1 > Pd-TUD-1 > Rh-TUD-1, with a total CO conversion at temperatures of 308, 328 and 348 K, respectively. The Au-TUD-1 exhibited a high stability and reusability as indicated by the observed high activity after ten continuous runs without any treatment. The outcomes of this research suggested that M-TUD-1 are promising nano-catalysts for the removal of the toxic CO gas and can also potentially be useful to protect the environment where a long-life time, cost-effectiveness and industrial scaling-up are the key approaches.

Adoption of Electronic Dental Records: Examining the Influence of Practice Characteristics on Adoption in One State.

Abstract: Objective Compared with medicine, less research has focused on adoption rates and factors contributing to the adoption of electronic dental records (EDRs) and certified electronic health records (EHRs) in the field of dentistry. We ran two multivariate models on EDR adoption and certification-capable EHR adoption to determine environmental and organizational factors associated with adoption. Methods We conducted telephone survey of a 10-item questionnaire using disproportionate stratified sampling procedure of 149 dental clinics in Tennessee in 2017 measuring adoption of dental information technology (IT) (EDRs and certification-capable EHRs) and practice characteristics, including region, rurality, specialty, and practice size. We used binomial logistic regression models to determine associations of adoption with predictor variables. Results A total of 77% of surveyed dental clinics in Tennessee had adopted some type of EDR system. When the definitions of certification capable were applied, the adoption rates in dental clinics dropped to 58%. A binomial logistic regression model for the effects of rurality, specialization, and practice size on the likelihood that a clinic would adopt an EHR product was statistically significant (chi-square (3) = 12.41, p = 0.0061). Of the three predictor variables, specialization and practice size were significant: Odds of adopting an EHR is 67% lower for specialists than for general dentists; and clinics with two or more practicing dentists were associated with a much greater likelihood of adopting an EHR-capable system (adjusted odds ratio = 3.09, p = 0.009). Conclusion Findings from this study indicate moderate to high levels of overall dental IT adoption. However, adoption rates in dental clinics do remain lower than those observed in office-based physician practices in medicine. Specialization and practice size were significant predictors of EHR-capable system adoption. Efforts to increase EHR adoption in dentistry should be mindful of potential disparities in smaller practices and between dental specialties and generalists.

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network

Abstract: In this paper, two algorithms are proposed for short-term PV power forecasting using a long short-term memory (LSTM) neural network (NN). The first algorithm is designed to predict a single step ahead PV power, whereas the latter is capable of forecasting time horizons with variable starting points, which makes it very useful for rolling horizon based energy management algorithms. The effect of the input sequence length on the performance of the single-step model is investigated. The prediction accuracy of the multi-step model is examined with different lengths of rolling prediction horizons. It is shown that in the case of intraday rolling horizons, adding certain new predictors can effectively improve the machine performance. Hourly and half hourly data from different seasons are used to train and test the performance of the forecasting machine. Moreover, to demonstrate the superiority of the proposed LSTM based algorithms, the performance of other neural networks, namely the generalized recurrent neural network (GRNN) and the nonlinear autoregressive exogenous (NARX) neural network, is also explored.