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- Di Luccio, Tiziana, et al.
(författare)
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Synthesis of CdS nanocrystals in polymeric films studied by in-situ GID and GISAXS
- 2015
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Ingår i: Insights for Energy Materials Using In-Situ Charaterization. - : Springer Science and Business Media LLC. - 0272-9172. - 9781510826625 ; 1810, s. 9-14
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Konferensbidrag (refereegranskat)abstract
- In this work, we describe the synthesis of CdS nanocrystals in thin polymeric films by in-situ Grazing Incidence Diffraction (GID) and Grazing Incidence Small Angle Scattering (GISAXS). The 2D GISAXS patterns indicate how the precursor structure is altered as the temperature is varied from 25°C to 300°C. At 150°C, the CdS nanocrystals start to arrange themselves in a hexagonal lattice with a lattice parameter of 27 A. The diffraction intensity from the hexagonal lattice reaches a maximum at 170"C and decreases steadily upon further heating above 220°C indicating loss of symmetry. Correspondingly, the GID scans at 170°C show strong crystalline peaks from cubic CdS nanocrystals that are about 2 nm size. The results indicate that a temperature of 170°C is sufficient to synthesize CdS nanocrystals without degradation of the polymer matrix (Topas) in thin films (about 30nm).
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- Elahe, M. F., et al.
(författare)
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Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle
- 2023
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.
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- Schoenrock, A., et al.
(författare)
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Efficient prediction of human protein-protein interactions at a global scale
- 2014
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Ingår i: Bmc Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. Results: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. Conclusions: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.
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