Alternative adjustment for seasonality and long-term time-trend in time-series analysis for long-term environmental exposures and disease counts [PDF]
BMC Medical Research Methodology, 2021 Background Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology.
Honghyok Kim +3 more
doaj +3 more sources
A long term time lapse microscopy technique for Arabidopsis roots [PDF]
Frontiers in Plant ScienceTime lapse microscopy is a transformative technique for plant cell and developmental biology. Light sheet microscopy, which manipulates the amount of light a sample is exposed to in order to minimize phototoxicity and maximize signal intensity, is an ...
Laura R. Lee +2 more
doaj +2 more sources
Unitary matrix models with a topological term and discrete time Toda equation [PDF]
, 1997 We study the full unitary matrix models. Introducing a new term $l log U$, l plays the role of the discrete time. On the other hand, the full unitary matrix model contains a topological term. In the continuous limit it gives rise to a phase transition at
Masato Hisakado
openalex +4 more sources
Long‐Term Time‐Varying Risk of Readmission After Acute Myocardial Infarction [PDF]
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 2018 Background Readmission after myocardial infarction (MI) is a publicly reported quality metric with hospital reimbursement linked to readmission rates.
Umesh N. Khot +9 more
doaj +2 more sources
The long-term time course of septic arthritis [PDF]
Bone & Joint OpenAims: The aims of this study were to: 1) report on a cohort of skeletally mature patients with native hip and knee septic arthritis over a 14-year period; 2) to determine the rate of joint failure in patients who had experienced an episode of hip or ...
Rhys G. E. Clement +4 more
doaj +2 more sources
Fuzzy inference-based LSTM for long-term time series prediction. [PDF]
Sci Rep, 2023 Long short-term memory (LSTM) based time series forecasting methods suffer from multiple limitations, such as accumulated error, diminishing temporal correlation, and lacking interpretability, which compromises the prediction performance.
Wang W, Shao J, Jumahong H.
europepmc +2 more sources
xLSTMTime: Long-Term Time Series Forecasting with xLSTM
AIIn recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing ...
Musleh Alharthi, Ausif Mahmood
doaj +2 more sources
Long-term time-series pollution forecast using statistical and deep learning methods. [PDF]
Neural Comput Appl, 2021 Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends ...
Nath P, Saha P, Middya AI, Roy S.
europepmc +2 more sources
Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping [PDF]
arXiv.org, 2023 Long-term time series forecasting has gained significant attention in recent years. While there are various specialized designs for capturing temporal dependency, previous studies have demonstrated that a single linear layer can achieve competitive ...
Zhe Li, Shiyi Qi, Yiduo Li, Zenglin Xu
semanticscholar +1 more source
Time Will Tell: the Short Term Gain and the Long Term Loss: Bariatric Surgery and Colorectal Cancer [PDF]
Iranian Journal of Colorectal Research, 2022 Obesity is a growing worldwide epidemic and its prevalence is in a continuous rise. Bariatric and metabolic surgery is the most effective treatment modality for long-term weight loss and resolution of associated comorbidities.
Melissa Kyriakos Saad +4 more
doaj +1 more source