324,724 research outputs found
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
Graphene q-switched Yb: phosphate glass channel waveguide laser
No full textQ-switched lasers can generate high-energy pulses that can have applications in medicine, material processing and defence. Waveguide lasers have several attractive features such as a low laser threshold and a high slope efficiency, provided that the propagation losses are kept low, compactness and mass-producibility. Ion-exchange is a simple and cheap technique to fabricate loss-loss waveguides in glass, with mode-locked operation being demonstrated in ion-exchanged Yb:phosphate glass lasers using a semiconductor saturable absorber mirror (SESAM). Using graphene as a saturable absorber has several key advantages over SESAMs such as a broad wavelength operating range, cost-effectiveness and ease of fabrication. Graphene has previously been used as a saturable absorber to demonstrate Q-switched mode-locking in a femtosecond-written glass waveguide laser and Q-switched operation in a carbon-irradiated Nd:YAG ceramic channel waveguide laser. In this paper we present an ion-exchanged Yb:phosphate glass waveguide laser, Q-switched using a graphene saturable absorber
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
sj-docx-2-tau-10.1177_17562872241241864 – Supplemental material for A phase III, single-arm, 6-month trial of a wide-dose range oral testosterone undecanoate product
No full textSupplemental material, sj-docx-2-tau-10.1177_17562872241241864 for A phase III, single-arm, 6-month trial of a wide-dose range oral testosterone undecanoate product by James S. Bernstein and Om P. Dhingra in Therapeutic Advances in Urology</p
Discomfort, Pressure Distribution and Safety in Operator's Seat-A Critical Review
No full textRosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is an Invited Paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 5 (2003): H. Dhingra, V. Tewari, and S. Singh. Discomfort, Pressure Distribution and Safety in Operator's Seat-A Critical Review. Vol. V. July 2003
Patient Safety in the World
No full textPatient safety is a fundamental principle of health care. However, many medical practices and risks associated with health care are emerging as major challenges for patient safety globally and contribute significantly to the burden of harm due to unsafe care. Available evidence suggests hospitalizations in low- and middle-income countries lead annually to 134 million adverse events, contributing to 2.6 million deaths. About 134 million adverse events worldwide give rise to 2.6 million deaths every year. Estimates indicate that in high-income countries, about 1 in 10 patients is harmed while receiving hospital care. This problem affects both high-income countries and low- and middle countries even if priorities and issues may differ. The most important adverse events concern medication procedures, healthcare-associated infections, surgical procedures, injection safety, blood transfusions, venous thromboembolism, sepsis, and diagnostic and radiation errors. Since 1999 when the Institute of Medicine (IOM) published its report “To err is human,” some progress has been made but patient harm is still a daily problem in healthcare. As a matter of fact, new threats are emerging due to population aging, along with new treatments and technologies which must be dealt with in addition to still-unresolved, long-standing problems. In this context, it is very important to adopt an international common strategy that creates networks, shares knowledge, programs, tools, good practices and develop and track indicators focusing on the specific priorities of each country and region
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
sj-docx-1-tau-10.1177_17562872241241864 – Supplemental material for A phase III, single-arm, 6-month trial of a wide-dose range oral testosterone undecanoate product
No full textSupplemental material, sj-docx-1-tau-10.1177_17562872241241864 for A phase III, single-arm, 6-month trial of a wide-dose range oral testosterone undecanoate product by James S. Bernstein and Om P. Dhingra in Therapeutic Advances in Urology</p
Pointwise and global well-posedness in set optimization: a direct approach
No full textThe aim of this paper is to characterize some of the pointwise and global wellposedness notions available in literature for a set optimization problem completely by compactness or upper continuity of an appropriate minimal solution set maps. The characterizations of compactness of set-valued maps, lead directly to many characterizations for well-posedness. Sufficient conditions are also given for global well-posedness
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