Results 101 to 110 of about 1,405,457 (345)

A System Dynamics Framework for Market Share Forecasting in the Telecommunications Market

Forecasting
This paper presents a novel system dynamics-based framework for forecasting market share evolution in the telecommunications sector. The framework conceptualizes market share as flows of subscribers—driven by churn, attraction, and market growth—between ...
Nikolaos Kanellos, Dimitrios Katsianis, Dimitris Varoutas   +2 more
doaj   +1 more source

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

FEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley   +1 more source

A New Loss Function for Enhancing Peak Prediction in Time Series Data with High Variability

Forecasting
Time series models are considered among the most intricate models in machine learning. Due to sharp temporal variations, time series models normally fall short in predicting the peaks or local minima accurately.
Mahan Hajiabbasi Somehsaraie, Soheyla Tofighi, Zhaoan Wang, Jun Wang, Shaoping Xiao   +4 more
doaj   +1 more source

A methionine‐lined active site governs carbocation stabilization and product specificity in a bacterial terpene synthase

FEBS Letters, EarlyView.
This study reveals a unique active site enriched in methionine residues and demonstrates that these residues play a critical role by stabilizing carbocation intermediates through novel sulfur–cation interactions. Structure‐guided mutagenesis further revealed variants with significantly altered product profiles, enhancing pseudopterosin formation. These
Marion Ringel, Carl P. O. Helmer, Shani Zev, Ronja Driller, Emily Buhr, Markus Reinbold, Renana Schwartz, Gabriel Foley, Mikael Boden, Daniel Garbe, Gerhard Schenk, Dan Thomas Major, Bernhard Loll, Thomas Brück   +13 more
wiley   +1 more source

A Novel k-Nearest Neighbors Approach for Forecasting Sub-Seasonal Precipitation at Weather Observing Stations

Forecasting
This study introduces a novel k-nearest neighbors (kNN) method of forecasting precipitation at weather-observing stations. The method identifies numerous monthly temporal patterns to produce precipitation forecasts for a specific month.
Sean Guidry Stanteen, Jianzhong Su, Paul Flanagan, Xunchang John Zhang   +3 more
doaj   +1 more source

ROC-Based Model Estimation for Forecasting Large Changes in Demand [PDF]

, 2013
Forecasting for large changes in demand should benefit from different estimation than that used for estimating mean behavior. We develop a multivariate forecasting model designed for detecting the largest changes across many time series. The model is fit
Gorr, Wilpen L, Schneider, Matthew J
core   +1 more source

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Decentralized Physical Infrastructure Networks (DePINs) for Solar Energy: The Impact of Network Density on Forecasting Accuracy and Economic Viability

Forecasting
This study explores the role of decentralized physical infrastructure networks (DePINs) in enhancing solar energy forecasting, focusing on how network density influences prediction accuracy and economic viability. Using machine learning models applied to
Marko Corn, Anže Murko, Primož Podržaj   +2 more
doaj   +1 more source

Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting

, 2011
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions.
Cheong, Michelle L. F., Choy, Murphy
core  

Defensive forecasting for optimal prediction with expert advice [PDF]

, 2007
The method of defensive forecasting is applied to the problem of prediction with expert advice for binary outcomes. It turns out that defensive forecasting is not only competitive with the Aggregating Algorithm but also handles the case of "second ...
Vovk, Vladimir
core   +4 more sources