Communication in troubled waters: responses of fish communication systems to changing environments

Fish populations are increasingly being subjected to anthropogenic changes to their sensory environments. The impact of these changes on inter- and intra-specific communication, and its evolutionary consequences, has only recently started to receive research attention. A disruption of the sensory environment is likely to impact communication, especially with respect to reproductive interactions that help to maintain species boundaries. Aquatic ecosystems around the world are being threatened by a variety of environmental stressors, causing dramatic losses of biodiversity and bringing urgency to the need to understand how fish respond to rapid environmental changes. Here, we discuss current research on different communication systems (visual, chemical, acoustic, electric) and explore the state of our knowledge of how complex systems respond to environmental stressors using fish as a model. By far the bulk of our understanding comes from research on visual communication in the context of mate selection and competition for mates, while work on other communication systems is accumulating. In particular, it is increasingly acknowledged that environmental effects on one mode of communication may trigger compensation through other modalities. The strength and direction of selection on communication traits may vary if such compensation occurs. However, we find a dearth of studies that have taken a multimodal approach to investigating the evolutionary impact of environmental change on communication in fish. Future research should focus on the interaction between different modes of communication, especially under changing environmental conditions. Further, we see an urgent need for a better understanding of the evolutionary consequences of changes in communication systems on fish diversity.

Prediction of Cranio-Maxilofacial Surgical Planning using an Inverse Soft Tissue Modelling Approach

In cranio-maxillofacial surgery, the determination of a proper surgical plan is an important step to attain a desired aesthetic facial profile and a complete denture closure. In the present paper, we propose an efficient modeling approach to predict the surgical planning on the basis of the desired facial appearance and optimal occlusion. To evaluate the proposed planning approach, the predicted osteotomy plan of six clinical cases that underwent CMF surgery were compared to the real clinical plan. Thereafter, simulated soft-tissue outcomes were compared using the predicted and real clinical plan. This preliminary retrospective comparison of both osteotomy planning and facial outlook shows a good agreement and thereby demonstrates the potential application of the proposed approach in cranio-maxillofacial surgical planning prediction.

Driving the refactoring of Java Enterprise applications by evaluating the distance between application elements

Java Enterprise Applications (JEAs) are complex systems composed using various technologies that in turn rely on languages other than Java, such as XML or SQL. Given the complexity of these applications, the need to reverse engineer them in order to support further development becomes critical. In this paper we show how it is possible to split a system into layers and how is possible to interpret the distance between application elements in order to support the refactoring of JEAs. The purpose of this paper is to explore ways to provide suggestions about the refactoring operations to perform on the code by evaluating the distance between layers and elements belonging those layers. We split JEAs into layers by considering the kinds and the purposes of the elements composing the application. We measure distance between elements by using the notion of the shortest path in a graph. Also we present how to enrich the interpretation of the distance value with enterprise pattern detection in order to refine the suggestion about modifications to perform on the code.

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive noisy optimization problems in a sequential framework, while offering some flexibility for advanced users. Besides, the implementation is done in a unified environment, making this package a useful device for studying the relative performances of existing approaches depending on the experimental setup. An overview of the package structure and interface is provided, as well as a description of the strategies and some insight about the implementation challenges and the proposed solutions. The strategies are compared to some existing optimization packages on analytical test functions and show promising performances.

Saccadic eye movement changes in Parkinson's disease dementia and dementia with Lewy bodies.

Neurodegeneration in Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB) affect cortical and subcortical networks involved in saccade generation. We therefore expected impairments in saccade performance in both disorders. In order to improve the pathophysiological understanding and to investigate the usefulness of saccades for differential diagnosis, saccades were tested in age- and education-matched patients with PDD (n = 20) and DLB (n = 20), Alzheimer's disease (n = 22) and Parkinson's disease (n = 24), and controls (n = 24). Reflexive (gap, overlap) and complex saccades (prediction, decision and antisaccade) were tested with electro-oculography. PDD and DLB patients had similar impairment in all tasks (P > 0.05, not significant). Compared with controls, they were impaired in both reflexive saccade execution (gap and overlap latencies, P < 0.0001; gains, P < 0.004) and complex saccade performance (target prediction, P < 0.0001; error decisions, P < 0.003; error antisaccades: P < 0.0001). Patients with Alzheimer's disease were only impaired in complex saccade performance (Alzheimer's disease versus controls, target prediction P < 0.001, error decisions P < 0.0001, error antisaccades P < 0.0001), but not reflexive saccade execution (for all, P > 0.05). Patients with Parkinson's disease had, compared with controls, similar complex saccade performance (for all, P > 0.05) and only minimal impairment in reflexive tasks, i.e. hypometric gain in the gap task (P = 0.04). Impaired saccade execution in reflexive tasks allowed discrimination between DLB versus Alzheimer's disease (sensitivity > or =60%, specificity > or =77%) and between PDD versus Parkinson's disease (sensitivity > or =60%, specificity > or =88%) when +/-1.5 standard deviations was used for group discrimination. We conclude that impairments in reflexive saccades may be helpful for differential diagnosis and are minimal when either cortical (Alzheimer's disease) or nigrostriatal neurodegeneration (Parkinson's disease) exists solely; however, they become prominent with combined cortical and subcortical neurodegeneration in PDD and DLB. The similarities in saccade performance in PDD and DLB underline the overlap between these conditions and underscore differences from Alzheimer's disease and Parkinson's disease.

QCD and Strongly Coupled Gauge Theories: Challenges and Perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.

Verbalization of Dependencies Between Concepts Built Through Fuzzy Cognitive Maps

The new computing paradigm known as cognitive computing attempts to imitate the human capabilities of learning, problem solving, and considering things in context. To do so, an application (a cognitive system) must learn from its environment (e.g., by interacting with various interfaces). These interfaces can run the gamut from sensors to humans to databases. Accessing data through such interfaces allows the system to conduct cognitive tasks that can support humans in decision-making or problem-solving processes. Cognitive systems can be integrated into various domains (e.g., medicine or insurance). For example, a cognitive system in cities can collect data, can learn from various data sources and can then attempt to connect these sources to provide real time optimizations of subsystems within the city (e.g., the transportation system). In this study, we provide a methodology for integrating a cognitive system that allows data to be verbalized, making the causalities and hypotheses generated from the cognitive system more understandable to humans. We abstract a city subsystem—passenger flow for a taxi company—by applying fuzzy cognitive maps (FCMs). FCMs can be used as a mathematical tool for modeling complex systems built by directed graphs with concepts (e.g., policies, events, and/or domains) as nodes and causalities as edges. As a verbalization technique we introduce the restriction-centered theory of reasoning (RCT). RCT addresses the imprecision inherent in language by introducing restrictions. Using this underlying combinatorial design, our approach can handle large data sets from complex systems and make the output understandable to humans.

A new measure for party coherence: Applying a physics-based concept to the Swiss party system

The paper revives a theoretical definition of party coherence as being composed of two basic elements, cohesion and factionalism, to propose and apply a novel empirical measure based on spin physics. The simultaneous analysis of both components using a single measurement concept is applied to data representing the political beliefs of candidates in the Swiss general elections of 2003 and 2007, proposing a connection between the coherence of the beliefs party members hold and the assessment of parties being at risk of splitting. We also compare our measure with established polarization measures and demonstrate its advantage with respect to multi-dimensional data that lack clear structure. Furthermore, we outline how our analysis supports the distinction between bottom-up and top-down mechanisms of party splitting. In this way, we are able to turn the intuition of coherence into a defined quantitative concept that, additionally, offers a methodological basis for comparative research of party coherence. Our work serves as an example of how a complex systems approach allows to get a new perspective on a long-standing issue in political science.