On Stochastic Error and Computational Efficiency of the Markov Chain Monte Carlo Method

In Markov Chain Monte Carlo (MCMC) simulations, the thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples. These samples are selected in accordance with the probability distribution function, known from the partition function of equilibrium state. As the stochastic error of the simulation results is significant, it is desirable to understand the variance of the estimation by ensemble average, which depends on the sample size (i.e., the total number of samples in the set) and the sampling interval (i.e., cycle number between two consecutive samples). Although large sample sizes reduce the variance, they increase the computational cost of the simulation. For a given CPU time, the sample size can be reduced greatly by increasing the sampling interval, while having the corresponding increase in variance be negligible if the original sampling interval is very small. In this work, we report a few general rules that relate the variance with the sample size and the sampling interval. These results are observed and confirmed numerically. These variance rules are derived for the MCMC method but are also valid for the correlated samples obtained using other Monte Carlo methods. The main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them

Use of Lagrangian simulations to hindcast the geographical position of propagule release zones in a Mediterranean coastal fish

The study of organism dispersal is fundamental for elucidating patterns of connectivity between populations, thus crucial for the design of effective protection and management strategies. This is especially challenging in the case of coastal fish, for which information on egg release zones (i.e. spawning grounds) is often lacking. Here we assessed the putative location of egg release zones of the saddled sea bream (Oblada melanura) along the south-eastern coast of Spain in 2013. To this aim, we hindcasted propagule (egg and larva) dispersal using Lagrangian simulations, fed with species-specific information on early life history traits (ELTs), with two approaches: 1) back-tracking and 2) comparing settler distribution obtained from simulations to the analogous distribution resulting from otolith chemical analysis. Simulations were also used to assess which factors contributed the most to dispersal distances. Back-tracking simulations indicated that both the northern sector of the Murcia region and some traits of the North-African coast were hydrodynamically suitable to generate and drive the supply of larvae recorded along the coast of Murcia in 2013. With the second approach, based on the correlation between simulation outputs and field results (otolith chemical analysis), we found that the oceanographic characteristics of the study area could have determined the pattern of settler distribution recorded with otolith analysis in 2013 and inferred the geographical position of main O. melanura spawning grounds along the coast. Dispersal distance was found to be significantly affected by the geographical position of propagule release zones. The combination of methods used was the first attempt to assess the geographical position of propagule release zones in the Mediterranean Sea for O. melanura, and can represent a valuable approach for elucidating dispersal and connectivity patterns in other coastal species

Expressive Policy Analysis with Enhanced System Dynamicity

Despite several research studies, the effective analysis of policy based systems remains a significant challenge. Policy analysis should at least (i) be expressive (ii) take account of obligations and authorizations, (iii) include a dynamic system model, and (iv) give useful diagnostic information. We present a logic-based policy analysis framework which satisfies these requirements, showing how many significant policy-related properties can be analysed, and we give details of a prototype implementation. Copyright 2009 ACM

Spatial genetic structure in the saddled sea bream (Oblada melanura [Linnaeus, 1758]) suggests multi-scaled patterns of connectivity between protected and unprotected areas in the Western Mediterranean Sea

Marine protected areas (MPAs) and networks of MPAs are advocated worldwide for the achievement of marine conservation objectives. Although the knowledge about population connectivity is considered fundamental for the optimal design of MPAs and networks, the amount of information available for the Mediterranean Sea is currently scarce. We investigated the genetic structure of the saddled sea bream ( Oblada melanura) and the level of genetic connectivity between protected and unprotected locations, using a set of 11 microsatellite loci. Spatial patterns of population differentiation were assessed locally (50-100 km) and regionally (500-1000 km), considering three MPAs of the Western Mediterranean Sea. All values of genetic differentiation between locations (Fst and Jost's D) were non-significant after Bonferroni correction, indicating that, at a relatively small spatial scale, protected locations were in general well connected with non-protected ones. On the other hand, at the regional scale, discriminant analysis of principal components revealed the presence of a subtle pattern of genetic heterogeneity that reflects the geography and the main oceanographic features (currents and barriers) of the study area. This genetic pattern could be a consequence of different processes acting at different spatial and temporal scales among which the presence of admixed populations, large population sizes and species dispersal capacity, could play a major role. These outcomes can have important implications for the conservation biology and fishery management of the saddled sea bream and provide useful information for genetic population studies of other coastal fishes in the Western Mediterranean Sea

Principled Entrepreneurship And Shared Leadership: The Case Of TEOCO (The Employee Owned Company)

This case describes a unique corporate culture, focused on employee ownership and employee-centered Human Resource practices, which fosters employee loyalty and motivates employee focus on organization objectives. The organization’s CEO and senior management team discuss in detail the company’s development strategy, the concept of shared leadership, and its strategic focus on Human Resource Management. Also emphasized is how the organization’s recent partnership with a private equity firm, and its acquisition of an international organization of similar size, may change TEOCO’s culture and its business model

Exploiting the Kronecker product structure of φ−functions in exponential integrators

Exponential time integrators are well-established discretization methods for time semilinear systems of ordinary differential equations. These methods use (Formula presented.) functions, which are matrix functions related to the exponential. This work introduces an algorithm to speed up the computation of the (Formula presented.) function action over vectors for two-dimensional (2D) matrices expressed as a Kronecker sum. For that, we present an auxiliary exponential-related matrix function that we express using Kronecker products of one-dimensional matrices. We exploit state-of-the-art implementations of (Formula presented.) functions to compute this auxiliary function's action and then recover the original (Formula presented.) action by solving a Sylvester equation system. Our approach allows us to save memory and solve exponential integrators of 2D+time problems in a fraction of the time traditional methods need. We analyze the method's performance considering different linear operators and with the nonlinear 2D+time Allen–Cahn equation