School of Accounting and Finance, University of Vaasa, Vaasa, Finland. This study investigates the determinants of long-term (up to ten years) venture success in the Dragons’ Den context using ...

A variety of linear models are available to represent common active electronic devices such as transistors and vacuum tubes. Devices operating under large-signal conditions often require nonlinear ...

Underwater radiated noise (URN) from vessels is an anthropogenic stressor that negatively affects marine ecosystems. Accordingly, the ability to estimate URN levels is a critical component for ...

Microbial community analysis typically involves determining which microbial features are associated with properties such as environmental or health phenotypes. This task is impeded by data ...

The deviations observed in the solubility of ibuprofen (IBP) and naproxen (NAP) in propylene glycol (PG) + water (W) cosolvent mixtures with respect to the logarithmic-linear model proposed by ...

Abstract: This article investigates the effect of irrational behaviors on the expected convergence speed to the Nash equilibrium (NE) set within the log-linear learning (LLL) model. First, a ...

Abstract: This study proposes a novel Mixed Integer Linear Programming (MILP) model for optimal management of Battery Energy Storage System (BESS) plants integrated with a real-time cycle counting ...

This repository now supports Structured Linear Controlled Differential Equations (SLiCEs), which replace the non-linear vector fields of NCDEs and Log-NCDEs with structured linear vector fields, ...

Forbes contributors publish independent expert analyses and insights. Caroline Castrillon covers career, entrepreneurship and women at work. Non-linear careers represent a fundamental shift in how we ...

In our last post, we talked about the assumptions of the linear regression model: linearity, homoscedasticity, independence, and normality. These assumptions serve as the foundation for building a ...

Linear regression has always been one of my most used machine learning models for understanding relationships between variables or estimating a variable from numerous factors. It’s simple, effective, ...