High water availability increases the negative impact of a native hemiparasite on its non-native host

Environmental factors alter the impacts of parasitic plants on their hosts. However, there have been no controlled studies on how water availability modulates stem hemiparasites' effects on hosts. A glasshouse experiment was conducted to investigate the association between the Australian native stem hemiparasite Cassytha pubescens and the introduced host Ulex europaeus under high (HW) and low (LW) water supply. Cassytha pubescens had a significant, negative effect on the total biomass of U. europaeus, which was more severe in HW than LW. Regardless of watering treatment, infection significantly decreased shoot and root biomass, nodule biomass, nodule biomass per unit root biomass, F-v/F-m, and nitrogen concentration of U. europaeus. Host spine sodium concentration significantly increased in response to infection in LW but not HW conditions. Host water potential was significantly higher in HW than in LW, which may have allowed the parasite to maintain higher stomatal conductances in HW. In support of this, the delta C-13 of the parasite was significantly lower in HW than in LW (and significantly higher than the host). C. pubescens also had significantly higher F-v/F-m and 66% higher biomass per unit host in the HW compared with the LW treatment. The data suggest that the enhanced performance of C. pubescens in HW resulted in higher parasite growth rates and thus a larger demand for resources from the host, leading to poorer host performance in HW compared with LW. C. pubescens should more negatively affect U. europaeus growth under wet conditions rather than under dry conditions in the field

Byzantine Gathering in Networks

This paper investigates an open problem introduced in [14]. Two or more mobile agents start from different nodes of a network and have to accomplish the task of gathering which consists in getting all together at the same node at the same time. An adversary chooses the initial nodes of the agents and assigns a different positive integer (called label) to each of them. Initially, each agent knows its label but does not know the labels of the other agents or their positions relative to its own. Agents move in synchronous rounds and can communicate with each other only when located at the same node. Up to f of the agents are Byzantine. A Byzantine agent can choose an arbitrary port when it moves, can convey arbitrary information to other agents and can change its label in every round, in particular by forging the label of another agent or by creating a completely new one. What is the minimum number M of good agents that guarantees deterministic gathering of all of them, with termination? We provide exact answers to this open problem by considering the case when the agents initially know the size of the network and the case when they do not. In the former case, we prove M=f+1 while in the latter, we prove M=f+2. More precisely, for networks of known size, we design a deterministic algorithm gathering all good agents in any network provided that the number of good agents is at least f+1. For networks of unknown size, we also design a deterministic algorithm ensuring the gathering of all good agents in any network but provided that the number of good agents is at least f+2. Both of our algorithms are optimal in terms of required number of good agents, as each of them perfectly matches the respective lower bound on M shown in [14], which is of f+1 when the size of the network is known and of f+2 when it is unknown

Constraint Based System-Level Diagnosis of Multiprocessors

Massively parallel multiprocessors induce new requirements for system-level fault diagnosis, like handling a huge number of processing elements in an inhomogeneous system. Traditional diagnostic models (like PMC, BGM, etc.) are insufficient to fulfill all of these requirements. This paper presents a novel modelling technique, based on a special area of artificial intelligence (AI) methods: constraint satisfaction (CS). The constraint based approach is able to handle functional faults in a similar way to the Russel-Kime model. Moreover, it can use multiple-valued logic to deal with system components having multiple fault modes. The resolution of the produced models can be adjusted to fit the actual diagnostic goal. Consequently, constrint based methods are applicable to a much wider range of multiprocessor architectures than earlier models. The basic problem of system-level diagnosis, syndrome decoding, can be easily transformed into a constraint satisfaction problem (CSP). Thus, the diagnosis algorithm can be derived from the related constraint solving algorithm. Different abstraction leveles can be used for the various diagnosis resolutions, employing the same methodology. As examples, two algorithms are described in the paper; both of them is intended for the Parsytec GCel massively parallel system. The centralized method uses a more elaborate system model, and provides detailed diagnostic information, suitable for off-line evaluation. The distributed method makes fast decisions for reconfiguration control, using a simplified model. Keywords system-level self-diagnosis, massively parallel computing systems, constraint satisfaction, diagnostic models, centralized and distributed diagnostic algorithms

Fast Probabilistic Consensus with Weighted Votes

International audienc

Continual On-Line Diagnosis of Hybrid Faults

An accurate system-state determination is essential in ensuring system dependability. An imprecise state assessment can lead to catastrophic failure through optimistic diagnosis, or underutilization of resources due to pessimistic diagnosis. Dependability is usually achieved through a fault detection, isolation and reconguration (FDIR) paradigm, of which the diagnosis procedure is a primary component. Fault resolution in on-line diagnosis is key to providing an accurate system-state assessment. Most diagnostic strategies are based on limited fault models that adopt either an optimistic (all faults s-a-X) or pessimistic (all faults Byzantine) bias. Our Hybrid Fault-Effects Model (HFM) handles a continuum of fault types that are distinguished by their impact on system operations. While this approach has been shown to enhance system functionality and dependability, on-line FDIR is required to make the HFM practical. In this paper, we develop a methodology for utilization of the system-st..

Visualization of software and systems as support mechanism for integrated software project control

Many software development organizations still lack support for obtaining intellectual control over their software development processes and for determining the performance of their processes and the quality of the produced products. Systematic support for detecting and reacting to critical process and product states in order to achieve planned goals is usually missing. One means to institutionalize measurement on the basis of explicit models is the development and establishment of a so-called Software Project Control Center (SPCC) for systematic quality assurance and management support. An SPCC is comparable to a control room, which is a well known term in the mechanical production domain. One crucial task of an SPCC is the systematic visualization of measurement data in order to provide context-, purpose-, and role-oriented information for all stakeholders (e.g., project managers, quality assurance managers, developers) during the execution of a software development project. The article will present an overview of SPCC concepts, a concrete instantiation that supports goal-oriented data visualization, as well as examples and experiences from practical applications