Molecular cloning of components of protective antigenic preparations has suggested that related parasite fatty acid-binding proteins could form the basis of the protective immune crossreactivity between the parasitic trematode worms Fasciola hepatica and Schistosoma mansoni. Molecular models of the two parasite proteins showed that both molecules adopt the same basic three-dimensional structure, consisting of a barrel-shaped molecule formed by 10 antiparallel beta-pleated strands joined by short loops, and revealed the likely presence of crossreactive, discontinuous epitopes principally derived from amino acids in the C-terminal portions of the molecules. A recombinant form of the S. mansoni antigen, rSm14, protected outbred Swiss mice by up to 67% against challenge with S. mansoni cercariae in the absence of adjuvant and without provoking any observable autoimmune response. The same antigen also provided complete protection against challenge with F. hepatica metacercariae in the same animal model. The results suggest that it may be possible to produce a single vaccine that would be effective against at least two parasites, F. hepatica and S. mansoni, of veterinary and human importance, respectively.

Distribution system problems, such as planning, loss minimization, and energy restoration, usually involve network reconfiguration procedures. The determination of an optimal network configuration is, in general, a combinatorial optimization problem. Several Evolutionary Algorithms (EAs) have been proposed to deal with this complex problem. Encouraging results have been achieved by using such approaches. However, the running time may be very high or even prohibitive in applications of EAs to large-scale networks. This limitation may be critical for problems requiring online solutions. The performance obtained by EAs for network reconfiguration is drastically affected by the adopted computational tree representation. Inadequate representations may drastically reduce the algorithm performance. Thus, the employed representation for chromosome encoding and the corresponding operators are very important for the performance achieved. An efficient data structure for tree representation may significantly increase the performance of evolutionary-based approaches for network reconfiguration problems. The present paper proposes a tree encoding and two genetic operators to improve the EA performance for network reconfiguration problems. The corresponding EA approach was applied to reconfigure large-scale systems. The performance achieved suggests that the proposed methodology can provide an efficient alternative for reconfiguration problems.

We present a probabilistic assessment of power distribution systems resilience under different weather conditions. It is based on Monte Carlo simulation considering failures and repairs models obtained in previous studies for the used power distribution system allowing consideration of different weather scenarios during the resilience estimation. Our main contribution is the methodology for assessing power distribution systems under different weather scenarios, which integrates time-to-event models to estimate resilience. The assessment is carried out on distribution networks of a Brazilian power company, and using two resilience perspectives: structural, considering networks connectivity and service, quantifying energy delivered. An investigation of which features are related to the resilience loss in extreme weather is also performed. Results show that all the networks are resilient to a normal weather condition in both perspectives and weather adversities reduce their structural resilience, mainly in large networks. At the same time, service resilience loss is not linearly related to network size, but with how electricity flows over the networks, as the average load passing through vertices and the presence of lateral branches. These findings can support decision making about management and planning of such systems as resilience-oriented modifications, resources allocation, and expansion planning.

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.

A large number of compiler optimizations are nowadays available to users. These optimizations interact with each other and with the input code in several and complex ways. The sequence of application of optimization passes can have a significant impact on the performance achieved. The effect of the optimizations is both platform and application dependent. The exhaustive exploration of all viable sequences of compiler optimizations for a given code fragment is not feasible. As this exploration is a complex and time-consuming task, several researchers have focused on Design Space Exploration (DSE) strategies both to select optimization sequences to improve the performance of each function of the application and to reduce the exploration time. In this article, we present a DSE scheme based on a clustering approach for grouping functions with similarities and exploration of a reduced search space resulting from the combination of optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method that is applied to a symbolic code representation. The data mining process combines three algorithms to generate clusters: the Normalized Compression Distance, the Neighbor Joining, and a new ambiguity-based clustering algorithm. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of optimization sequences in the context of the ReflectC compiler, considering 49 compilation passes while targeting a Xilinx MicroBlaze processor, and aiming at performance improvements for 51 functions and four applications. Experimental results reveal that the use of our clustering-based DSE approach achieves a significant reduction in the total exploration time of the search space (20× over a Genetic Algorithm approach) at the same time that considerable performance speedups (41% over the baseline) were obtained using the optimized codes. Additional experiments were performed considering the LLVM compiler, considering 124 compilation passes, and targeting a LEON3 processor. The results show that our approach achieved geometric mean speedups of 1.49 × , 1.32 × , and 1.24 × for the best 10, 20, and 30 functions, respectively, and a global improvement of 7% over the performance obtained when compiling with -O2.