Every year about 30 million people travel by ship worldwide often in extreme weather conditions and polluted environments and many other factors that impact the health of passengers and crew staff.Such issues require medical staff for passenger health care.We introduce a model based on Reinforcement learning(RL) which is used in the dialogue system.We incorporate the Hierarchical reinforcement learning (HRL) model with the layers of Deep Q-Network for dialogue oriented diagnosis system.Policy learning is integrated as policy gradients are already defined.We created a two-stage hierarchical strategy.We used the hierarchical structure with double-layer policies for automatic disease diagnosis.A double layer means it splits the task into sub-tasks named high-state strategy and low-level strategy.It has a user simulator component that communicates with the patient for symptom collection low-level agents inquire about symptoms.Once it's done collecting it sends results to the high-level agent which activates the D-classifier for the last diagnosis.When it's done its sent back by the user simulator to patients to verify the diagnosis made.Every single diagnosis made has its reward that trains the system

The allocation of healthcare resources on ships is crucial for safety and well-being due to limited access to external aid.Proficient medical staff on board provide a mobile healthcare facility, offering a range of services from first aid to complex procedures.This paper presents a system model utilizing Reinforcement Learning (RL) to optimize doctor-patient assignments and resource allocation in maritime settings.The RL approach focuses on dynamic, sequential decision-making, employing Q-learning to adapt to changing conditions and maximize cumulative rewards.Our experimental setup involves a simulated healthcare environment with variable patient conditions and doctor availability, operating within a 24-hour cycle.The Qlearning algorithm iteratively learns optimal strategies to enhance resource utilization and patient outcomes, prioritizing emergency cases while balancing the availability of medical staff.The results highlight the potential of RL in improving healthcare delivery on ships, demonstrating the system's effectiveness in dynamic, time-constrained scenarios and contributing to overall maritime safety and operational resilience.

Abstract We are assisting to a paradigmatic shift in developing Web applications since their components are often distributed and deployed as services among different organizations. Their logic is based on a set of actions that can be linked together by exploiting higher level languages more suitable to address the scale of the Web. On this multi‐organization scale, applications can be influenced by different context events generated by the environment where they run. Handling these events requires run‐time adaptations of the application's behaviour to react, properly and quickly, to changes. The paper addresses these needs by proposing a programming paradigm based on “autonomic service compositions,” ie, compositions that are able to self‐change their structure, according to a specific life cycle, to allow for the continuation of execution, even if unexpected events arise. The approach exploits autonomic computing and reasoning for taking decision on information collected during processes execution. Autonomic actions on composition structures are performed using Event Condition Action rules and a set of meta‐operations. The approach is detailed, analysed, and discussed with reference to some examples derived from a real‐world application.

Composition represents today one of the most challenging approach to design complex software systems, especially in distributed environments. While two different views (in time and in space) are considered by researchers to compose applications, we aim at applying these two views in an integrated approach. In particular, we believe that large-scale composition in an open world is simplified by using composition in time, whereas, in the closed environment of a cluster, composition in space is more effective. In the paper, we propose the integration of a workflow engine with ProActive objects to support together the two kinds of composition at different scale-level. The paper shows the first results of this integration and highlights the plan for achieving a stronger integration with the use of GCM components.

Asynchronous invocation and continuation are common patterns in some middleware infrastructures for object-based distributed computing. Their benefits are particularly significant in distributed environments characterized by high communication latencies and coarse-grained operations. Therefore, Web services could strongly benefit from the adoption of these patterns to (1) overlap communication with computation, (2) reduce the high number of interactions typically needed to handle stateless services by migrating the state of a service as parameters of service operations, (3) intercept at run-time data dependencies among consecutive services in a composition not visible from service descriptions. Unfortunately, current semantics of Web services do not directly support the patterns, but some specifications (i.e. WS-addressing) can simplify their implementation. In the paper we present the patterns, their benefits, and a module that implements a flexible schema useful to perform asynchronous invocations in several contexts. This way, modelling composed services can benefit from abstractions whilst more sophisticated low-level interactions among services are automatically handled at run-time.

Web Services are becoming more and more fundamental building blocks of Web-based distributed applications and a core technology for grid systems. Due to their flexibility, Web Services easily combine, in a common and coherent framework, ubiquitous computing with heterogeneous applications composed of different kinds of resources and, typically distributed in many organizations. We expect that this technology will follow the same evolution paths that have characterized other technologies so far, with some specificity due to the openness and size of the application context. In this connection, optimizations tied to invocations and workflows are assuming a primary role in Web Services research. The synchronous request/reply nature of the most diffused underling protocol (HTTP) introduces several restrictions in many application scenarios. On the other hand, asynchronous interactions are allowed by using message oriented middleware platforms, like JMS, which are typically harder to handle than object- and process-oriented middleware. In this paper, we propose a first implementation of a module that allows for dynamic Web Services invocations, which, on the basis of metadata added to WSDL, is able to select the most appropriate invocation technique for calling a Web Services operation.

Grid computing is becoming a prominent field of interest for distributed systems. The capability to support resource sharing between different organizations and the high level of performance are noteworthy features. However, to define, deploy and execute grid applications require significant design effort and complex resource coordination, which can be alleviated by using workflow management systems. This technology is more and more adopted in distributed systems and grids since it ensures easiness and flexibility at design-time and performance and dynamicity at run-time. This paper gives a contribution to performance improvements of workflow by presenting a technique to exploit fine-grained concurrency at run-time. The technique is implemented in a workflow enactment service that dynamically optimizes process execution limiting the design effort of application developers

Workflow enactment systems are becoming an effective solution to ease programming, deployment and execution of distributed applications in several domains such as telecommunication, manufacturing, e-business, e-government and grid computing. In some of these fields, efficiency and traffic optimization represent key aspects for a wide diffusion of workflow engines and modeling tools. This paper focuses on a technique that enables fine-grained concurrency in compute and data-intensive workflows and reduces the traffic on the network by limiting the number of interactions to the ones strictly needed to bring the data where they are really necessary for continuing the flow of computations. We implemented this technique by using the concepts of wait by necessity and automatic continuation and we integrated it in a flexible, Java workflow engine that through the new mechanisms is able to navigate a workflow anticipating the enactment of sequential activities