Preface; 1. Universal Scaling Relations for Logarithmic-Correction Exponents R. Kenna; 1. Introduction; 2. Scaling Relations at Second-Order Phase Transitions; 2.1. Leading scaling behaviour; 2.2. Scaling relations for leading exponents; 2.3. Logarithmic scaling corrections; 2.4. Scaling relations for logarithmic exponents; 3. Standard Derivation of Leading Scaling Relations; 3.1. Static scaling; 3.2. Renormalization group; 3.3. Fisher's scaling relation; 3.4. The shift exponent; 4. Logarithmic Corrections; 4.1. Static correction exponents; 4.2. Hyperscaling for logarithms.

4.3. Logarithmic counterpart to Fisher's relation4.4. Corrections to the logarithmic scaling relations; 4.5. The logarithmic shift exponent; 5. Fisher Renormalization for Logarithmic Corrections; 6. Logarithmic Correction Exponents for Various Models; 6.1. q = 4 d = 2 Potts model; 6.2. O(N) 4d theory; 6.3. Long-range O(N) 4d theory; 6.4. Spin glasses in 6 dimensions; 6.5. Percolation in 6 dimensions; 6.6. Lee-yang problem in 6 dimensions and lattice animals in 8 dimensions; 6.7. Ising model in 2 dimensions; 6.8. Quenched-disordered Ising model in 2 dimensions.

6.9. Ashkin-Teller model in 2 dimensions6.10. Spin models on networks; 7. Conclusions; Acknowledgements; Appendix A. Homogeneous Functions; References; 2. Phase Behaviour and Criticality in Primitive Models of Ionic Fluids O.V. Patsahan and I.M. Mryglod; 1. Introduction; 2. Historical Background; 3. Location of the Gas-Liquid Critical Point of the RPM; 3.1. Computer simulations; 3.2. Generalized Debye-Huckel theory; 3.3. Associating mean-spherical approximation (AMSA); 4. The KSSHE Theory; 5. The Method of Collective Variables. Links to Other Theories.

5.1. A two-component charge-asymmetric PM6. Gas-Liquid Separation in Asymmetric PMs: The Method of CVs; 6.1. Charge-asymmetric PMs; 6.2. Size and charge asymmetric PMs; 6.3. Monovalent PMs with size asymmetry; 6.4. Size- and charge-asymmetric PMs; 7. Critical Behaviour of the "Coulombic Systems": Theoretical Studies; 7.1. The effective Hamiltonian of the RPM in the vicinity of the gas-liquid critical point; 7.2. Crossover behaviour in fluids with Coulomb interactions; 7.3. Charge-charge correlations in the models of ionic fluids; 8. Conclusions; Acknowledgement; References.

3. Monte Carlo Simulations in Statistical Physics- From Basic Principles to Advanced Applications W. Janke1. Introduction; 2. The Monte Carlo Method; 2.1. Random sampling; 2.2. Importance sampling; 2.3. Local update algorithms; 2.3.1. Metropolis algorithm; 2.3.2. Glauber algorithm; 2.3.3. Heat-bath algorithm; 2.4. Temporal correlations; 2.5. Cluster algorithms; 2.5.1. Swendsen-Wang multiple-cluster algorithm; 2.5.2. Wolff single-cluster algorithm; 2.5.3. Embedded cluster algorithm; 2.5.4. Performance of cluster algorithms; 2.5.5. Improved estimators.