Results 41 to 50 of about 522,783 (291)

Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting

Entropy, 2021
Traditional machine-learning methods are inefficient in capturing chaos in nonlinear dynamical systems, especially when the time difference Δt between consecutive steps is so large that the extracted time series looks apparently random.
Xiangyi Meng, Tong Yang
doaj   +1 more source

Structural insights into an engineered feruloyl esterase with improved MHET degrading properties

FEBS Letters, EarlyView.
A feruloyl esterase was engineered to mimic key features of MHETase, enhancing the degradation of PET oligomers. Structural and computational analysis reveal how a point mutation stabilizes the active site and reshapes the binding cleft, expading substrate scope.
Panagiota Karampa, Konstantinos Makryniotis, Theofani‐Iosifina Sousani, Evangelos Topakas, Vangelis Daskalakis, Maria Dimarogona   +5 more
wiley   +1 more source

Iterative Forecasting of Short Time Series

Applied Sciences
We 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

Peramalan Data Kunjungan Wisatawan Mancanegara ke Indonesia menggunakan Fuzzy Time Series

JEPIN (Jurnal Edukasi dan Penelitian Informatika), 2019
Wisatawan mancanegara memegang peranan penting terhadap pertumbuhan ekonomi dari sektor pariwisata. Untuk meningkatkan kunjungan wisatawan mancanegara perlu dilakukan pembangunan yang berkelanjutan pada sektor pariwisata. Pembangunan yang dilakukan harus
Indra Jiwana Thira, Nissa Almira Mayangky, Desiana Nur Kholifah, Imanuel Balla, Windu Gata   +4 more
doaj   +1 more source

Time Series Forecasting With Volatility Activation Function

IEEE Access, 2022
Time series forecasting is the method of predicting future values of a model by reviewing its past data. Various models like traditional approaches, statistical methods, moving average, ARIMA, RNN’s, or XGBoost may also be applied.
Furkan Kayim, Atinc Yilmaz
doaj   +1 more source

PARP inhibition and pharmacological ascorbate demonstrate synergy in castration‐resistant prostate cancer

Molecular Oncology, EarlyView.
Pharmacologic ascorbate (vitamin C) increases ROS, disrupts cellular metabolism, and induces DNA damage in CRPC cells. These effects sensitize tumors to PARP inhibition, producing synergistic growth suppression with olaparib in vitro and significantly delayed tumor progression in vivo. Pyruvate rescue confirms ROS‐dependent activity.
Nicolas Gordon, Peter T. Gallagher, Orly I. Richter, Neermala Poudel Neupane, Amy C. Mandigo, Jennifer J. McCann, Emanuela Dylgjeri, Irina Vasilevskaya, Christopher McNair, Channing J. Paller, Wm. Kevin Kelly, Karen E. Knudsen, Matthew J. Schiewer, Ayesha A. Shafi   +13 more
wiley   +1 more source

Short-Term Forecasting of Non-Stationary Time Series

Engineering Proceedings
Forecasting 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, Antonio Liotta, Alexander Jacob, Muhammad Azfar Yaqub   +3 more
doaj   +1 more source

Plecstatin inhibits hepatocellular carcinoma tumorigenesis and invasion through cytolinker plectin

Molecular Oncology, EarlyView.
The ruthenium‐based metallodrug plecstatin exerts its anticancer effect in hepatocellular carcinoma (HCC) primarily through selective targeting of plectin. By disrupting plectin‐mediated cytoskeletal organization, plecstatin inhibits anchorage‐dependent growth, cell polarization, and tumor cell dissemination.
Zuzana Outla, Jan Kosla, Magdalena Prechova, Lukas Frick, Patricia Bortel, Yasmin Borutzki, Andrea Bileck, Christopher Gerner, Samuel M. Meier‐Menches, Selma Osmanagic‐Myers, Martin Gregor   +10 more
wiley   +1 more source

Forecasting Hierarchical Time Series in Power Generation

Energies, 2020
Academic attention is being paid to the study of hierarchical time series. Especially in the electrical sector, there are several applications in which information can be organized into a hierarchical structure.
Tiago Silveira Gontijo, Marcelo Azevedo Costa   +1 more
doaj   +1 more source

Advanced Time Series Forecasting Methods [PDF]

, 2012
The researchers from various fields have been studying on time series forecasting for approximately one century in order to get better forecasts for the future.To achieve high forecast accuracy level, various time series forecasting approaches have been improved in the literature.
Aladag C.H., Eğrioğlu E.
openaire   +2 more sources