DIOXOLANE NORBORNANE / NORBORNENE COMPOUNDS SUITABLE AS ANTIMICROBIAL AGENTS TO TREAT BACTERIAL INFECTIONS

The invention provides a compound including : A core having a first face and a second face; A binding portion attached to the first face of the core, wherein the binding portion is capable of binding to an anionic group present in a cell membrane of a microorganism; and A hydrophobic portion attached to the second face of the core, wherein the hydrophobic portion is capable of interacting with the cell membrane of the microorganism; and The core comprises a dioxolane norbornane / norbornene of formula (II): Or a salt or ion thereof, wherein R' is a moiety forming part of a hydrophobic portion; R2 is a first binding portion; and R3 is a seconding binding portion. The invention also provides compositions including at least one such compound. The invention also provides methods and uses for treatment or prophylaxis of infection of a mammal by a microorganism, and methods and uses for treating or preventing contamination of a substrate by a microorganism, using the compounds and compositions.

Flock inspired area coverage using wireless boid-like sensor agents

Simulated flocking is achievable using three boid rules [13]. We propose an area coverage model inspired by Reynolds’ flocking algorithm, investigating strategies for achieving quality coverage using flocking rules. Our agents are identical and autonomous, using only local sensory information for indirect communication. Upon deployment, agents are in the default separation mode. The cohesion rule would then guarantee that agents remain within the swarm, covering spaces with explored neighbour spaces. Four experiments are conducted to evaluate our model in terms of coverage quality achieved. We firstly investigate agents’ separation speed before the speed with which isolated agents re-organizes is investigated. The third experiment compares coverage quality achieved using our model with coverage quality achieved using random guessing. Finally, we investigate fault tolerance in the event of agents’ failures. Our model exhibits good separation and cohesion speed, achieving high quality coverage. Additionally, the model is fault tolerant and adaptive to agents’ failures.

The relationship between emergence of the shortest path and information value using ant-like agents

Ant-like agents forage between two points. These agents' probabilistic movements are based on the use of two pheromones; one marking trails towards the goal and another marking trails back to the starting point. Path selection decisions are influenced by the relative levels of attractive and repulsive pheromone in each agent's local environment. Our work in [5] evaluates three pheromone perception strategies, investigating path formation speed, quality, directionality, robustness and adaptability under different parameter settings(degree of randomness, pheromone evaporation rate and pheromone diffusion rate). We re-evaluate two of these strategies in terms of the amount of information they provide using Shannon's formulation [3, 4, 8, 9, 12, 14, 15, 16, 17]. We determine information as the difference between uncertainty before and after path selection decisions. Our focus in this paper is on investigating relationships between the emergence of the shortest path and the amount of stigmergic information that exists in the form of pheromone. Agents are deployed centrally and emergence measures are determined using the worst, reference and best cases observed in [5]. Additionally, the amount of local and global information that is available to agents in each movement step is evaluated. Furthermore, Pearson's correlation coefficients between measures of emergence and the amount of information are calculated. The significance of these correlation coefficients is tested using a 2 tailed test at 1% level of significance. Consequently the relationship between the amount of information and emergent behaviour is established. Significant relationships between information and the emergence of the shortest path exist when strong emergent behaviour is present.

A probabilistic movement model for shortest path formation in virtual ant-like agents

We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.

Development and validation of the Agents' Socially Desirable Responding (ASDR) scale

While the marketing discipline has advanced knowledge by augmenting objective measures of marketing performance with survey-solicited subjective measures, it has not adequately attended to potential response biases. The authors’ review of the literature highlights the need for a valid measure of socially desirable responding that can be used in detecting social desirability bias in managers’ responses to subjective performance measures. Agents’ Socially Desirable Responding (ASDR) scale development and validation procedures are described. Validation efforts reveal the psychometric properties of the eight-item ASDR scale and provide evidence of its utility.

KHA-CARI guideline : biochemical and haematological targets : haemoglobin concentrations in patients using erythropoietin-stimulating agents

This is an update of a previous CARI Guideline on management of anaemia in CKD patients.

Mobile agents security protocols

Mobile agents are expected to run in partially unknown and untrustworthy environments. They transport from one host to another host through insecure channels and may execute on non-trusted hosts. Thus, they are vulnerable to direct security attacks of intruders and non-trusted hosts. The security of information the agents collect is a fundamental requirement for a trusted implementation of electronic business applications and trade negotiations. This chapter discusses the security protocols presented in the literature that aim to secure the data mobile agents gather while searching the Internet, and identifies the security flaws revealed in the protocols. The protocols are analyzed with respect to the security properties, and the security flaws are identified. Two recent promising protocols that fulfill the various security properties are described. The chapter also introduces common notations used in describing security protocols and describes the security properties of the data that mobile agents gather.

Security framework for mobile agents-based applications

Mobile agents have been proposed for key applications such as forensics analysis, intrusion detection, e-commerce, and resource management. Yet, they are vulnerable to various security threats by malicious hosts or intruders. Conversely, genuine platforms may run malicious agents. It is essential to establish a truly secure framework for mobile agents to gain trust of clients in the system. Failure to accomplish a trustworthy secured framework for Mobile Agent System (MAS) will limit their deployment into the key applications. This chapter presents a comprehensive taxonomy of various security threats to Mobile Agent System and the existing implemented security mechanisms. Different mechanisms are discussed, and the related security deficiencies are highlighted. The various security properties of the agent and the agent platform are described. The chapter also introduces the properties, advantages, and roles of agents in various applications. It describes the infrastructure of the system and discusses several mobile agent frameworks and the accomplished security level.

Learning other agents' preferences in multi-agent negotiation using the bayesian classifier

In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

Learning other agents' preferences in multiagent negotiation

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents' preferences via past interactions. Over time, the agents can incrementally update their models of other agents' preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situations where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

Negotiating agents that learn about others’ preferences

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents’ preferences via past interactions. Over time, the agents can incrementally update their models of other agents’ preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situation where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

A framework for co-ordination and learning among teams of agents

We present a framework for team coordination under incomplete information based on the theory of incomplete information games. When the true distribution of the uncertainty involved is not known in advance, we consider a repeated interaction scenario and show that the agents can learn to estimate this distribution and share their estimations with one another. Over time, as the set of agents' estimations become more accurate, the utility they can achieve approaches the optimal utility when the true distribution is known, while the communication requirement for exchanging the estimations among the agents can be kept to a minimal level.

Agents in adversarial domains - modelling environments in parallel

We present a model of an environment to evaluate the behavior of an agent trying to hide from a pursuer is presented. The model computes the direction and the amount of protection provided by the environment. The computational complexity of this problem is improved by using a parallel implementation of this algorithm.