Currently, the collaboration in scientific communities has been studied in order to explain, among other things, the knowledge diffusion. The quality of Graduate Programmes is often associated with the scientific coll...Currently, the collaboration in scientific communities has been studied in order to explain, among other things, the knowledge diffusion. The quality of Graduate Programmes is often associated with the scientific collaboration. This paper discusses how scientific collaboration processes can be identified and characterized through social and complex networks. For this purpose, collaboration networks of bibliographic production, research projects, and committees of PhD theses and Masters’ dissertations by researchers from a graduate program in computational modeling were studied. The data were obtained from CAPES’ reports of the period from 2001 to 2009. Among the studied indices, centrality indices indicate the presence of prominent researchers who influence others and promptly interact with other researchers in the network. The indices of complex networks reveal the presence of the small-world (i.e. these networks are favorable to increase coordination between researchers) phenomenon and indicate a behavior of scale-free degree distribution (i.e. some researchers promote clustering more than others) for one of the studied networks.展开更多
The use of computational intelligence has become commonplace for accurate wind speed and energy forecasting,however the energy-intensive processes involved in training and tuning stands as a critical issue for the sus...The use of computational intelligence has become commonplace for accurate wind speed and energy forecasting,however the energy-intensive processes involved in training and tuning stands as a critical issue for the sustainability of AI models.Quantum computing emerges as a key player in addressing this concern,offering a quantum advantage that could potentially accelerate computations or,more significantly,reduce energy consumption.It is a matter of debate if purely quantum machine learning models,as they currently stand,are capable of competing with the classical state of the art on relevant problems.We investigate the efficacy of quantum neural networks(QNNs)for wind speed nowcasting,comparing them to a baseline Multilayer Perceptron(MLP).Utilizing meteorological data from Bahia,Brazil,we develop a QNN tailored for up to six hours ahead wind speed prediction.Our analysis reveals that the QNN demonstrates competitive performance compared to MLP.We evaluate models using RMSE,Pearson’s R,and Factor of 2 metrics,emphasizing QNNs’promising generalization capabilities and robustness across various wind prediction scenarios.This study is a seminal work on the potential of QNNs in advancing renewable energy forecasting,advocating for further exploration of quantum machine learning in sustainable energy research.展开更多
基金financial support from CNPq(the Brazilian federal grant agency).
文摘Currently, the collaboration in scientific communities has been studied in order to explain, among other things, the knowledge diffusion. The quality of Graduate Programmes is often associated with the scientific collaboration. This paper discusses how scientific collaboration processes can be identified and characterized through social and complex networks. For this purpose, collaboration networks of bibliographic production, research projects, and committees of PhD theses and Masters’ dissertations by researchers from a graduate program in computational modeling were studied. The data were obtained from CAPES’ reports of the period from 2001 to 2009. Among the studied indices, centrality indices indicate the presence of prominent researchers who influence others and promptly interact with other researchers in the network. The indices of complex networks reveal the presence of the small-world (i.e. these networks are favorable to increase coordination between researchers) phenomenon and indicate a behavior of scale-free degree distribution (i.e. some researchers promote clustering more than others) for one of the studied networks.
基金partially funded by the project“Master’s and PhD in Quantum Technologies-QIN-FCRH-2025-5-1-1”in supported by QuIIN-Quantum Industrial Innovation,EMBRAPII CIMATEC Com-petence Center in Quantum Technologiesfinancial resources from the PPI IoT/Manufatura 4.0 of the MCTI grant number 053/2023,signed with EMBRAPII+1 种基金National Council for Scientific and Technological Development(CNPq,Brazil),for partially funding this work.Erick G.Sperandio Nascimento is a CNPq techno-logical development fellow(Proc.308963/2022-9)the Surrey Institute for People-Centred AI at the University of Surrey(UK)for their institutional support.
文摘The use of computational intelligence has become commonplace for accurate wind speed and energy forecasting,however the energy-intensive processes involved in training and tuning stands as a critical issue for the sustainability of AI models.Quantum computing emerges as a key player in addressing this concern,offering a quantum advantage that could potentially accelerate computations or,more significantly,reduce energy consumption.It is a matter of debate if purely quantum machine learning models,as they currently stand,are capable of competing with the classical state of the art on relevant problems.We investigate the efficacy of quantum neural networks(QNNs)for wind speed nowcasting,comparing them to a baseline Multilayer Perceptron(MLP).Utilizing meteorological data from Bahia,Brazil,we develop a QNN tailored for up to six hours ahead wind speed prediction.Our analysis reveals that the QNN demonstrates competitive performance compared to MLP.We evaluate models using RMSE,Pearson’s R,and Factor of 2 metrics,emphasizing QNNs’promising generalization capabilities and robustness across various wind prediction scenarios.This study is a seminal work on the potential of QNNs in advancing renewable energy forecasting,advocating for further exploration of quantum machine learning in sustainable energy research.
