Editors Overview

rtm maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

Focus and Scope

About the Journal

Recent Trends in Mathematics is a peer-reviewed open-access academic Journal dedicated to exploring and disseminating cutting-edge developments, novel concepts, and emerging trends in the field of mathematics. The Journal serves as a platform for mathematicians, researchers, and scholars to share their contributions to the evolving landscape of mathematical sciences. It aims to foster collaboration and inspire innovation in the mathematical community by showcasing the most recent and influential trends in mathematics.

Focus and Scope

  • Pure Mathematics:
    • Abstract algebra: group theory, ring theory, field theory, module theory, linear algebra, multilinear algebra, homological algebra, category theory, representation theory, operator algebras.
    • Number theory: prime numbers, integer properties, Diophantine equations, analytic number theory, algebraic number theory, computational number theory, cryptography.
    • Real analysis: functions, limits, continuity, differentiation, integration, measure theory, functional analysis, Fourier analysis, complex analysis.
    • Topology: general topology, algebraic topology, differential topology, geometric topology, homotopy theory, knot theory, homology theory, cohomology theory.
  • Applied Mathematics:
    • Mathematical Modeling: System modeling, optimization models, population models, financial models, weather models, climate models, disease models, traffic models, network models, game theory models.
    • Computational Mathematics: Numerical methods, finite element methods, finite difference methods, Monte Carlo methods, spectral methods, optimization algorithms, machine learning algorithms, data analysis algorithms, high-performance computing.
    • Numerical Analysis: Error analysis, stability analysis, convergence analysis, approximation theory, interpolation, integration, differentiation, linear algebra algorithms, nonlinear equation solvers.
    • Optimization: Linear programming, nonlinear programming, constrained optimization, convex optimization, combinatorial optimization, multi-objective optimization, dynamic programming, stochastic optimization.
    • Differential Equations: Ordinary differential equations, partial differential equations, initial value problems, boundary value problems, eigenvalue problems, numerical methods for solving differential equations.
  • Probability & Statistics:
    • Probability Theory: Measure theory, probability spaces, random variables, distributions, expectation, variance, moment generating functions, convergence in probability, law of large numbers, central limit theorem, conditional probability, independence, stochastic processes.
    • Statistical Inference: Point estimation, confidence intervals, hypothesis testing, p-values, statistical tests (e.g., t-test, ANOVA, chi-square), maximum likelihood estimation, Bayesian inference, Markov chain Monte Carlo methods, decision theory, nonparametric statistics, asymptotic theory.
    • Stochastic Processes: Markov chains, Brownian motion, Poisson processes, queuing theory, renewal theory, branching processes, martingales, stochastic differential equations, time series analysis, survival analysis.
    • Data Analysis: Exploratory data analysis, descriptive statistics, visualization techniques, regression analysis (linear, logistic, etc.), classification, clustering, dimensionality reduction, statistical learning methods, model selection, statistical computing software (e.g., R, Python), big data analysis.
    • Bayesian Methods: Prior distributions, posterior distributions, likelihood functions, Bayes’ theorem, Markov chain Monte Carlo methods, Bayesian networks, Bayesian decision theory, Bayesian applications in various fields (e.g., finance, ecology, biostatistics).
  • Geometry & Topology:
    • Differential Geometry: Curves, surfaces, manifolds, tensors, Riemannian geometry, geodesics, curvature, connections, parallel transport, differential forms, integration on manifolds, Lie groups, Lie algebras.
    • Algebraic Topology: Homology theory, cohomology theory, homotopy theory, fundamental groups, singular homology, simplicial homology, cellular homology, CW complexes, spectral sequences, de Rham cohomology, singular cohomology, Čech cohomology, K-theory.
    • Geometric Analysis: Elliptic partial differential equations, harmonic maps, Ricci flow, minimal surfaces, geometric measure theory, symplectic geometry, contact geometry, Kaehler geometry, complex geometry, complex manifolds.
    • Manifold Theory: Smooth manifolds, differentiable manifolds, topological manifolds, embeddings, immersions, submanifolds, diffeomorphisms, symplectic manifolds, contact manifolds, Kaehler manifolds, complex manifolds.
  • Mathematical Physics:
    • Quantum Mechanics: Hilbert spaces, operators, wave functions, Schrödinger equation, Heisenberg uncertainty principle, quantum entanglement, spin, path integrals, canonical quantization, bra-ket notation, density matrices, quantum field theory (axiomatic and perturbative approaches).
    • General Relativity: Spacetime geometry, Einstein field equations, black holes, gravitational waves, cosmology, differential geometry, Riemannian geometry, Lorentzian manifolds, causal structure, singularity theorems.
    • Mathematical Methods in Physics: Variational calculus, Green’s functions, complex analysis, Fourier analysis, group theory, Lie groups, Lie algebras, representation theory, operator theory, functional analysis, partial differential equations, integral equations, asymptotic analysis.
    • Quantum Field Theory: Renormalization group, path integrals, Feynman diagrams, perturbation theory, gauge theories, Standard Model of particle physics, quantum chromodynamics, electroweak theory, quantum gravity, string theory.
  • Combinatorics & Discrete Mathematics:
    • Graph Theory: Graphs, networks, trees, colorings, matchings, connectivity, graph algorithms (e.g., Dijkstra’s, BFS, DFS), random graphs, extremal graph theory, topological graph theory, algebraic graph theory.
    • Combinatorial Structures: Permutations, combinations, partitions, generating functions, recurrence relations, Ramsey theory, extremal combinatorics, design theory, coding theory, matroids, polytopes.
    • Coding Theory: Error-correcting codes, linear codes, cyclic codes, block codes, convolutional codes, LDPC codes, turbo codes, decoding algorithms, information theory, cryptography.
    • Discrete Optimization: Discrete Optimization

Keywords

  • Interdisciplinary applications
  • Emerging fields
  • Machine learning and mathematics
  • Data-driven modeling
  • Computational mathematics
  • Mathematical modeling
  • Optimization
  • Probability and statistics
  • Network theory
  • Complex systems
  • Artificial intelligence
  • Quantum computing
  • Pedagogical trends
  • Educational technology in mathematics
  • Mathematics outreach