1,355,094 research outputs found
Antony Papadia, \u2715
No full textAntony Papadia was born in Caracas Venezuela. He attained his Associates degree from Valencia College and transferred to the University of Central Florida in 2012. Currently, He is pursuing a Bachelor\u27s degree in Biology, with a minor in Psychology. He volunteers with the marine coastal Oyster/Mangrove restoration project with the biology department\u27s Ceelab team. Furthermore, His research involves transferring live successful Oyster Mats to areas where Oysters are not commonly found, and monitoring their growth, larvae recruitment, and survival. His main research interest is determining the effects of biological and human derived toxins, on the health, physiology, and adaptation of marine animal species, specifically on marine vertebrates. Additionally, he would like to apply the solutions found on affected marine animal populations. He plans to attain his doctorate to further his understanding of animal physiology and aid in the survival of marine species.https://stars.library.ucf.edu/mcnair_gallery/1040/thumbnail.jp
A numerical procedure for machining distortions simulation on a SAF 2507 casting workpiece
No full textThe workpiece distortion that occurs during machining, can lead to a large increase in the number of the scrap parts. Residual stresses are the main cause of these distortions and they are generally present in both forging and casting products. In order to obtain the desired microstructure and mechanical properties, the workpiece is subjected to heat treatment before being worked. Quenching produces residual stresses that exist throughout a large percentage of the casting or forging part. Distortion occurs as a result of removing stressed material from the workpiece. The component will re-equilibrate and distort as each layer of stressed material is machined away. This paper describes a procedure development for distortions numerical analysis on a SAF2507 casting bulk workpiece. A solubilization heat treatment has been simulated, in order to predict the bulk residual stresses distribution. Different metal cutting processes have been considered to measure the numerical distortions induced in the workpiece
Virtual tryout and optimization of the extrusion process using a shape variables generator integrated in the CAE pre-processing environment
No full textUdine (Italy
Laparoscopic indocyanine green sentinel lymph node mapping in endometrial cancer. Papadia A, Imboden S, Siegenthaler F, Gasparri ML, Mohr S, Lanz S, Mueller MD.
No full textAbstract
BACKGROUND:
In endometrial cancer (EMCA), indocyanine green (ICG) sentinel lymph node (SLN) mapping has been reported, mainly in conjunction with robotic surgery.
OBJECTIVE:
We aimed to evaluate detection rates, sensitivity, and false negative (FN) rate of laparoscopic ICG SLN mapping in EMCA, and to evaluate differences in surgical outcomes between patients subjected to SLN biopsy only versus lymphadenectomy.
METHODS:
A retrospective analysis of EMCA patients undergoing ICG SLN mapping ± pelvic (PLND) and/or para-aortic lymphadenectomy (PALND) was performed. Detection rates were calculated for the entire cohort. Sensitivity and FN rates were calculated for patients undergoing lymphadenectomy after SLN mapping, and surgical outcome was compared among patients undergoing SLN mapping only versus lymphadenectomy.
RESULTS:
Of 75 patients, 33 underwent SLN mapping and 42 underwent SLN mapping followed by PLND/PALND. Overall and bilateral detection rates were 96 % (72/75) and 88 % (66/75), respectively, and the median number of removed SLNs, pelvic non-SLNs (NSLN) and para-aortic NSLNs was 3, 27, and 19, respectively. With a FN rate of 8.3 %, only one patient had bilateral FN SLNs and a metastatic para-aortal NSLN. Estimated blood loss (EBL) and operative (OR) time were significantly lower in patients undergoing SLN mapping only. No differences in complication rates between patients undergoing SLN mapping only and patients undergoing lymphadenectomy were recorded.
CONCLUSIONS:
Laparoscopic ICG SLN mapping has excellent overall and bilateral detection rates and a low FN rate. Compared with lymphadenectomy, SLN biopsy is associated with significantly lower EBL and shorter OR time
On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting
No full textFeatured Application: The objective of this study is to explore the potential advantages of combining statistical modeling with Long Short-Term Memory (LSTM) and Bayesian Optimization (BO) algorithms for time-series forecasting in the context of Photovoltaic Power Forecasting (PVPF) implementation with limited input information. Our analysis revealed that integrating these methods resulted in more accurate forecasting outcomes than using each method separately. The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field
recensione a E. Papadia, La forza dei sentimenti. Anarchici e socialisti in Italia (1870-1900), il Mulino, Bologna, 2019
No full textLa recensione prende in esame l'educazione sentimentale alla politica di socialisti e anarchici ricostruita da Elena Papadia
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