Global warming impact on climate change in Serbia for the period 1961-2100

Serbia is situated at Balkan Peninsula, and currently majority of the territory is under warm temperate fully humid climate type with warm summers (Cfb type, according to Koppen-Geiger Climate Classification). Observed changes in climate conditions since 1961 until present time show significant increase in temperature change and change in precipitation patterns. Disturbances in heat conditions, which are recorded to affect human health, agricultural production and forest ecosystem, are priority in climate change analysis and application in adaptation planning. Future change analysis show accelerated increase of temperature by the end of the 21st century, which proves the needs for immediate measures for mitigation of negative impacts. Temperature increase averaged over the territory of Serbia is 1.2 degrees C for the period 1996-2015 with respect to the period 1961-1980, with highest increase of maximum daily temperature during the summer season, 2.2 degrees C. Using high resolution multi-model ensemble approach for analysis of the future changes with respect to the base period 1986-2005, in compliance with Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (ARS), it is estimated that temperature may increase by 1.9 degrees C according to Representative Concentration Pathway 4.5 (RCP4.5) scenario and by 4.4 degrees C according to RCP8.5 by the end of the century. Spatial distribution of temperature increase, intensification of high precipitation events and decrease of summer precipitation, show intrusion of subtropical climate over the Serbia and increase of high temperature and high precipitation risks. Results presented in this paper, using high-resolution multi-model ensemble approach, provide climate change information for short term to long term planning in different sectors of economy and preservation of human health and environment

Random Costs in Combinatorial Optimization

The random cost problem is the problem of finding the minimum in an exponentially long list of random numbers. By definition, this problem cannot be solved faster than by exhaustive search. It is shown that a classical NP-hard optimization problem, number partitioning, is essentially equivalent to the random cost problem. This explains the bad performance of heuristic approaches to the number partitioning problem and allows us to calculate the probability distributions of the optimum and sub-optimum costs.Comment: 4 pages, Revtex, 2 figures (eps), submitted to PR

Number partitioning as random energy model

Number partitioning is a classical problem from combinatorial optimisation. In physical terms it corresponds to a long range anti-ferromagnetic Ising spin glass. It has been rigorously proven that the low lying energies of number partitioning behave like uncorrelated random variables. We claim that neighbouring energy levels are uncorrelated almost everywhere on the energy axis, and that energetically adjacent configurations are uncorrelated, too. Apparently there is no relation between geometry (configuration) and energy that could be exploited by an optimization algorithm. This ``local random energy'' picture of number partitioning is corroborated by numerical simulations and heuristic arguments.Comment: 8+2 pages, 9 figures, PDF onl

Cyber Insurance: recent advances, good practices & challenges

The aim of this ENISA report is to raise awareness for the most impact to market advances, by shortly identifying the most significant cyber insurance developments for the past four years – during 2012 to 2016 – and to capture the good practices and challenges during the early stages of the cyber insurance lifecycle, i.e. before an actual policy is signed, laying the ground for future work in the area

Landscape Encodings Enhance Optimization

Hard combinatorial optimization problems deal with the search for the minimum cost solutions (ground states) of discrete systems under strong constraints. A transformation of state variables may enhance computational tractability. It has been argued that these state encodings are to be chosen invertible to retain the original size of the state space. Here we show how redundant non-invertible encodings enhance optimization by enriching the density of low-energy states. In addition, smooth landscapes may be established on encoded state spaces to guide local search dynamics towards the ground state