Results 51 to 60 of about 17,777,604 (295)
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Introduction This study investigated the safety and efficacy of single‐needle Rheocarna therapy for chronic limb‐threatening ischemia (CLTI) with wounds. Methods Six patients with CLTI involving ulcers unresponsive to revascularization underwent single‐needle Rheocarna treatment.
Yasutaka Yamauchi +9 more
wiley +1 more source
, 2015 In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily ...
Teixeira, João Paulo +1 more
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source
Forecasting for Stationary Binary Time Series
Acta Applicandae Mathematica, 2003 The forecasting problem for a stationary and ergodic binary time series $\{X_n\}_{n=0}^{\infty}$ is to estimate the probability that $X_{n+1}=1$ based on the observations $X_i$, $0\le i\le n$ without prior knowledge of the distribution of the process $\{X_n\}$. It is known that this is not possible if one estimates at all values of $n$.
Gusztáv Morvai, Benjamin Weiss 0002
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Iterative Forecasting of Short Time Series
Applied SciencesWe forecast short time series iteratively using a model based on stochastic differential equations. The recorded process is assumed to be consistent with an α-stable Lévy motion.
Evangelos Bakalis
doaj +1 more source
TIME SERIES FORECASTING USING NEURAL NETWORKS [PDF]
Challenges of the Knowledge Society, 2013 Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function.
BOGDAN OANCEA, ŞTEFAN CRISTIAN CIUCU
doaj
Dense Sampling of Time Series for Forecasting
IEEE Access, 2022 A time series contain a large amount of information suitable for forecasting. Classical statistical and recent deep learning models have been widely used in a variety of forecasting applications.
Il-Seok Oh, Jin-Seon Lee
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
wiley +1 more source
Short-Term Forecasting of Non-Stationary Time Series
Engineering ProceedingsForecasting climate events is crucial for mitigating and managing risks related to climate change; however, the problem of non-stationarity in time series (NTS) arises, making it difficult to capture and model the underlying trends.
Amir Aieb +3 more
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Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source