Self-organized coordinated motion in groups of physically connected robots.

An important goal of collective robotics is the design of control systems that allow groups of robots to accomplish common tasks by coordinating without a centralized control. In this paper, we study how a group of physically assembled robots can display coherent behavior on the basis of a simple neural controller that has access only to local sensory information. This controller is synthesized through artificial evolution in a simulated environment in order to let the robots display coordinated-motion behaviors. The evolved controller proves to be robust enough to allow a smooth transfer from simulated to real robots. Additionally, it generalizes to new experimental conditions, such as different sizes/shapes of the group and/or different connection mechanisms. In all these conditions the performance of the neural controller in real robots is comparable to the one obtained in simulation.     

Design of selective eglin inhibitors of HCV NS3 proteinase

Hepatitis C virus (HCV) infection is a major health problem that leads to cirrhosis and hepatocellular carcinoma in a substantial number of infected individuals, estimated to be 100-200 million worldwide. Unfortunately, immunotherapy or other effective treatments for HCV infection are not yet available, and interferon administration has limited efficacy. Different approaches to HCV therapy are being explored, and these include inhibition of the viral proteinase, helicase, and RNA-dependent RNA polymerase and development of a vaccine. Here we present the design of selective inhibitors with nanomolar potencies of HCV NS3 proteinase based on eglin c. These eglin c mutants were generated by reshaping the inhibitor active site-binding loop, and the results emphasize the role played by residues P5-P4' in enzyme recognition. In addition, alanine scanning experiments provide evidence that the N terminus of eglin c also contributes to NS3 binding. These eglin inhibitors offer a unique tool for accurately assessing the requirements for effective inhibition of the enzymatic activity of NS3 and at the same time can be considered lead compounds for the identification of other NS3 inhibitors in targeted design efforts.     

Pathology of the Pericarbon bovine pericardial xenograft implanted in humans

Reviews psychosocial interventions for child and adolescent conduct problems, including oppositional defiant disorder and conduct disorder, to identify empirically supported treatments. Eighty-two controlled research studies were evaluated using the criteria developed by the Division 12 (Clinical Psychology) Task Force on Promotion and Dissemination of Psychological Procedures. The 82 studies were also examined for specific participant, treatment, and methodological characteristics to describe the treatment literature for child and adolescent conduct problems. Two interventions were identified that met the stringent criteria for well-established treatments: videotape modeling parent training program (Spaccarelli, Cotler, & Penman, 1992; Webster-Stratton, 1984, 1994) and parent-training programs based on Patterson and Gullion's (1968) manual Living With Children (Alexander & Parsons, 1973; Bernal, Klinnert, & Schultz, 1980; Wiltz & Patterson, 1974). Twenty of the 82 studies were identified as supporting the efficacy of probably efficacious treatments.     

Causes for transfer of obstetric patients to an intensive care unit (1990-19960

BACKGROUND: In order to have an updated review of the causes for transferring patients from our Institute to Intensive Care Unit (ICU), a study has been conducted on patients admitted from 1990 to 1996. METHODS: Of the 28 women transferred, one underwent dilatation & curettage of uterine cavity after abortion, 3 had a normal delivery and 24 underwent a cesarean section. RESULTS AND CONCLUSIONS: The complications were as follows: respiratory failure (5 cases); suspected or confirmed lung embolism (1); cerebral ischemia (1); septic complications (1); cardiocirculatory complications (19 cases; 7 of them were due to DIC; 6 due to cardiocirculatory failure; 6 due to haemorrhagic shock). These patients were divided into 3 groups, those with pre-existing pathologies which worsened (2 cases); those due to deterioration of pathology arose during pregnancy (17) and, finally, complications during delivery (9). In total the cases of mortality were 7, all of them following cardiocirculatory complications.     

Maritime repositioning of empty containers under uncertain port disruptions

This paper addresses the problem of repositioning empty containers in maritime networks under possible port disruptions. Since drastically different futures may occur, the decision making process for dealing with this problem cannot ignore the uncertain nature of its parameters. In this paper, we consider the uncertainty of relevant problem data by a stochastic programming approach, in which different scenarios are included in a multi-scenario optimization model and linked by non-anticipativity conditions. Numerical experiments show that the multi-scenario solutions provide a hedge against uncertainty when compared to deterministic decisions and exhibit some forms of robustness, which mitigate the risks of not meeting empty container demand.     

A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems

In the last years, the numerous successful applications of fuzzy rule-based systems (FRBSs) to several different domains have produced a considerable interest in methods to generate FRBSs from data. Most of the methods proposed in the literature, however, focus on performance maximization and omit to consider FRBS comprehensibility. Only recently, the problem of finding the right trade-off between performance and comprehensibility, in spite of the original nature of fuzzy logic, has arisen a growing interest in methods which take both the aspects into account. In this paper, we propose a Pareto-based multi-objective evolutionary approach to generate a set of Mamdani fuzzy systems from numerical data. We adopt a variant of the well-known (2+2) Pareto Archived Evolutionary Strategy ((2+2)PAES), which adopts the one-point crossover and two appropriately defined mutation operators. (2+2)PAES determines an approximation of the optimal Pareto front by concurrently minimizing the root mean squared error and the complexity. Complexity is measured as sum of the conditions which compose the antecedents of the rules included in the FRBS. Thus, low values of complexity correspond to Mamdani fuzzy systems characterized by a low number of rules and a low number of input variables really used in each rule. This ensures a high comprehensibility of the systems. We tested our version of (2+2)PAES on three well-known regression benchmarks, namely the Box and Jenkins Gas Furnace, the Mackey-Glass chaotic time series and Lorenz attractor time series datasets. To show the good characteristics of our approach, we compare the Pareto fronts produced by the (2+2)PAES with the ones obtained by applying a heuristic approach based on SVD-QR decomposition and four different multi-objective evolutionary algorithms.     

Multiphase Flow in Deforming Porous Media: A Review

In this work we present a general model for the analysis of multiphase flow in deforming porous media with particular regard to concrete and biological tissues. Such problems are typically multi-physics ones with overlapping domains where diffusion, advection, adsorption, phase change, deformation, chemical reactions and other phenomena take place in the porous medium. For the analysis of such a complex system, the model here proposed is obtained from microscopic scale by applying the thermodynamically constrained averaging theory which guarantees the satisfaction of the second law of thermodynamics for all constituents both at micro and macro-level. Furthermore, one can obtain some important thermodynamic restrictions for the evolution equations describing the material deterioration. Two specific forms of the general model adapted to the cases of cementitious and biological materials respectively are shown. Some numerical simulations aimed at proving the validity of the approach adopted, are also presented and discussed.     

Autonomous construction using scarce resources in unknown environments

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a “real but contrived” experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real world.     

Enhancing DLV instantiator by backjumping techniques

Disjunctive logic programming (DLP) is a powerful formalism for knowledge representation and reasoning. The high expressiveness of DLP language, together with the recent availability of some efficient DLP system, has favoured the application of DLP in emerging areas like Knowledge Management and Information Integration. These applications have often to deal with huge input data, and have evidenced the need to improve the efficiency of DLP instantiators. Program instantiation is the first phase of a DLP computation; in this phase, variables are replaced by constants to generate a ground program which is then evaluated by propositional algorithms in the second phase of the computation. The instantiation process may be computationally expensive, and in fact its efficiency has been recognized to be a key issue for solving real-world problems by using disjunctive logic programming. Given a program P, a good instantiation for P is a ground program P′ having precisely the same answer sets as P and such that: (1) P′ can be computed efficiently from P, and (2) P′ does not contain “useless” rules, (P′ is as small as possible) and can thus be evaluated efficiently. In this paper, we present a structure-based backjumping algorithm for the instantiation of disjunctive logic programs, that meets the above requirements. In particular, given a rule r to be grounded, our algorithm exploits both the semantical and the structural information about r for computing efficiently the ground instances of r, avoiding the generation of “useless” rules. That is, from each general rule r, we compute only a relevant subset of its ground instances, avoiding the generation of “useless” instances, while fully preserving the semantic of the program. We have implemented this algorithm in DLV—the state-of-the-art implementation of DLP—and we have carried out an experimentation activity on an ample collection of benchmark problems. The experimental results are very positive: the new technique improves sensibly the efficiency of the DLV system on many program classes.