Bioenvironmental Modelling and Optimization

Advancing Mathematical Modelling and Optimization in Biological and Environmental Systems.

Bioenvironmental Modelling and Optimization (BEMO) is an international, peer-reviewed journal publishing original research on mathematical model development, dynamical systems analysis, optimization and optimal control, numerical and computational methods, statistical inference, inverse problems, and scientific machine learning for biological and environmental systems.

Author Guidelines & Submission

About the Journal

Journal Abbreviation	BEMO
Publisher	PROPanoramic Solution
Publication Model	Double-Anonymous Peer Review, Open Access
Publication Frequency	Biannual (June & December)
Online ISSN	XXXX-XXXX
Print ISSN	XXXX-XXXX
DOI Prefix	10.XXXXX
Publication Language	English
Scientific Discipline	Applied Mathematics and Mathematical Modelling

Journal Abbreviation

BEMO

Publisher

PROPanoramic Solution

Publication Model

Double-Anonymous Peer Review,

Open Access

Publication Frequency

Biannual (June & December)

Online ISSN

XXXX-XXXX

Print ISSN

XXXX-XXXX

DOI Prefix

10.XXXXX

Publication Language

English

Scientific Discipline

Applied Mathematics and Mathematical Modelling

Research Focus and Scope

BEMO welcomes mathematically rigorous and original research contributions in six interconnected areas of mathematical modelling, mathematical analysis, optimization, statistical inference, and scientific computation.

Mathematical Model Development

Development of deterministic, stochastic, and nonlinear mathematical models, including ordinary differential equations (ODEs), partial differential equations (PDEs), delay differential equations, and fractional-order models.

Mathematical Analysis of Dynamical Systems

Analysis of equilibrium points, stability, bifurcations, nonlinear dynamics, periodic solutions, and the long-term behaviour of dynamical systems.

Mathematical Optimization and Optimal Control

Optimization of mathematical models, optimal control, dynamic optimization, constrained optimization, and model-based intervention strategies.

Numerical and Computational Mathematical Modelling

Numerical solution of mathematical models, computational simulations, parameter estimation, sensitivity analysis, model calibration, and validation.

Statistical Modelling, Inference, and Inverse Problems

Statistical and probabilistic approaches to mathematical modelling, parameter inference, inverse problems, uncertainty quantification, and the calibration of biological and environmental dynamical systems.

Data-Driven and Scientific Machine Learning for Mathematical Modelling

Integration of data-driven methods and scientific machine learning with mathematical models, including physics-informed learning, differential equation discovery, surrogate modelling, and hybrid mechanistic-data-driven approaches for biological and environmental systems.

Application domains: BEMO particularly encourages mathematically rigorous studies addressing biological and environmental systems, including cancer dynamics, epidemiology, population dynamics, ecology, ecological interactions, biodiversity conservation, environmental processes, and sustainable resource management. Statistical inference and scientific machine learning approaches are within scope when they make substantive contributions to mathematical modelling, analysis, identification, or optimization of these systems.