1,721,092 research outputs found
Solar PV Power Forecasting and Ageing Evaluation Using Machine Learning Techniques
No full textSolar photovoltaic (PV) power forecasting is a crucial aspect of efficient energy management in the renewable energy sector. This study examines the use of artificial neural networks (ANNs) to forecast solar PV power output. It considers various factors influencing power output and investigates different ANNs for prediction. Real-world PV power data is collected and preprocessed for training and testing ANNs such as recurrent neural networks, autoencoders, and convolutional neural networks. The results show that ANNs, particularly Long Short-term memory (LSTM), accurately forecast PV power output in the short term. The study also analyzes the impact of panel ageing on PV power using machine learning models, revealing effective prediction of performance degradation. Clustering the dataset into sunny and cloudy subsets, and using separate models for each subset improves prediction accuracy. The study presents a comprehensive analysis of ANNs for PV power forecasting and the influence of panel ageing, highlighting the potential of machine learning for precise and reliable predictions
Modelling Ageing and Power Production of Solar PV Using Machine Learning Techniques
No full textSolar photovoltaic (PV) power prediction plays a pivotal role in optimizing energy management within the re-newable energy industry. In this investigation, we explore the utilization of artificial neural networks (ANNs) to model solar PV ageing and, at the same time, forecast power generation. Diverse factors impacting power output are examined, and multiple ANNs are explored for prediction purposes. Real-world PV power data is collected and subjected to preprocessing to facilitate the training and testing of ANNs, including recurrent neural networks, autoencoders, and convolutional neural networks. The findings demonstrate the accurate short-term forecasting capa-bilities of ANNs, with particular emphasis on Long Short-term Memory (LSTM) networks. Additionally, the study delves into the effects of panel ageing on PV power by leveraging machine learning models and data analysis, leading to the identification of effective performance degradation prediction. The dataset is further segmented into subsets representing sunny and cloudy conditions, and employing separate models for each subset yields improved prediction accuracy. In fact, notable distinctions in power production characteristics between sunny and cloudy con-ditions are revealed. Thus, tailoring distinct models for different weather conditions is crucial to ensure precise power predictions and effectively address daily uncertainties. The research presents an extensive analysis of ANNs for PV power forecasting and emphasizes the potential of machine learning techniques in enabling accurate and reliable predictions
Probabilistic Forecasting of PV Power Using Artificial Neural Networks with Confidence Intervals
No full textThis paper outlines an innovative approach to enhance the predictability of solar photovoltaic power. By employing advanced machine learning techniques, specifically artificial neural networks, this study addresses the challenges posed by the intermittent nature of solar energy. The employed models incorporate probabilistic forecasting to provide not only precise power output predictions but also confidence intervals that signify the uncertainty in these predictions. This approach supports more effective integration of solar energy into power grids, facilitating better energy management and planning. The results indicate that our models can significantly improve the accuracy of solar power forecasting, crucial for optimizing grid operations and enhancing renewable energy adoption
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Reduced cyclooxygenase 2 levels after hypoxia exposure result in the loss of immunoprivilege of allogeneic mesenchymal stem cells
No full textAllogenic mesenchymal stem cells (MSCs) from young and healthy donors have demonstrated the potential to treat multiple degenerative disorders. However, recent pre-clinical and clinical studies
report that immunogenicity and poor survival of transplanted MSCs impairs the efficacy of cells for regenerative applications. According to these studies, initially immunoprivileged under in vitro conditions, MSCs are targeted by the host immune system after transplantation in the ischemic tissues.
We performed in vitro and in vivo (rat myocardial infarction [MI] model) studies using bone-marrow derived MSCs to elucidate the mechanisms responsible for the switch of MSCs from immunoprivileged to immunogenic state, under ischemic conditions. MSCs were cultured under normal or hypoxic conditions (0.4%O2, 24hr), and the levels of immunosuppressive molecules cyclooxygenase-2 (COX2, rate limiting enzyme for PGE2 synthesis) and prostaglandin E2 (PGE2) were assessed using western blot and ELISA, respectively. Our results show that the levels of COX2 and PGE2 decreased in MSCs following exposure to hypoxia. Activation of immune response was evaluated using flow cytometry analysis after
co-culturing leukocytes with MSCs lacking, or over-expressing COX2. We also found that proteasome-mediated degradation of COX2 in hypoxic MSCs is responsible for PGE2 decrease and loss of immunoprivilege of MSCs. Finally, when COX2 over-expressing MSCs were transplanted in rat MI model we found that maintaining COX2 levels improved their survival in vivo. While investigating the
mechanisms involved in COX2 degradation, we found that interaction of COX2 with COP9 signalosome subunit 5 (CSN5) under normoxia prevents its degradation by the proteasome. On the other hand,
hypoxia mediated decrease in the levels of CSN5 and, its reduced binding to COX2, makes COX2 protein susceptible to proteasome-mediated degradation. This subsequently causes PGE2 downregulation and loss of immunoprivilege of MSCs. Our results provide novel mechanistic evidence that PGE2 is downregulated in hypoxic MSCs causing post-transplantation rejection of allogeneic MSCs. This suggests that new strategies that target CSN5-COX2 signaling may improve survival and utility of transplanted allogeneic MSCs in the ischemic heart
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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