We present COSMIC, a leader-driven multiagent coordination framework that extends the AutoGen GroupChat paradigm to support more stable and context-consistent collaboration among large language model (LLM) agents. COSMIC introduces: 1) a leader agent that decomposes tasks and issues an explicit termination decision; 2) a shared rolling memory buffer that synchronizes recent context across agents; and 3) a speaker selector agent (SSA) that enforces structured turn-taking and mitigates self-invocation and speaker imbalance. Across six coordination-intensive tasks, COSMIC achieves 100% validity under task-specific hard-constraint criteria. On the five tasks where baseline frameworks produced validated outputs, COSMIC improves composite scores by 20.2%, 23.3%, 27.7%, 52.6%, and 46.9% over CrewAI Hierarchical, CrewAI Sequential, AutoGen, LangChain, and OpenAI Swarm, respectively. COSMIC reduces wall-clock time relative to CrewAI Hierarchical, CrewAI Sequential, AutoGen, and LangChain by 57.3%, 50.8%, 61.4%, and 77.6%, while incurring higher runtime than OpenAI Swarm due to richer coordination and memory overhead. On GSM8K, COSMIC attains$94\pm 1$% accuracy, exceeding the sparse multiagent debate (MAD) configuration reported in a Google Research publication. Empirically, performance peaks around five agents, reflecting a trade-off between diversity and coordination overhead.

We present a cloud-based approach for the design of interoperable electronic health record (EHR) systems. Cloud computing environments provide several benefits to all the stakeholders in the healthcare ecosystem (patients, providers, payers, etc.). Lack of data interoperability standards and solutions has been a major obstacle in the exchange of healthcare data between different stakeholders. We propose an EHR system - cloud health information systems technology architecture (CHISTAR) that achieves semantic interoperability through the use of a generic design methodology which uses a reference model that defines a general purpose set of data structures and an archetype model that defines the clinical data attributes. CHISTAR application components are designed using the cloud component model approach that comprises of loosely coupled components that communicate asynchronously. In this paper, we describe the high-level design of CHISTAR and the approaches for semantic interoperability, data integration, and security.

We present a novel framework, CloudView, for storage, processing and analysis of massive machine maintenance data, collected from a large number of sensors embedded in industrial machines, in a cloud computing environment. This paper describes the architecture, design, and implementation of CloudView, and how the proposed framework leverages the parallel computing capability of a computing cloud based on a large-scale distributed batch processing infrastructure that is built of commodity hardware. A case-based reasoning (CBR) approach is adopted for machine fault prediction, where the past cases of failure from a large number of machines are collected in a cloud. A case-base of past cases of failure is created using the global information obtained from a large number of machines. CloudView facilitates organization of sensor data and creation of case-base with global information. Case-base creation jobs are formulated using the MapReduce parallel data processing model. CloudView captures the failure cases across a large number of machines and shares the failure information with a number of local nodes in the form of case-base updates that occur in a time scale of every few hours. At local nodes, the real-time sensor data from a group of machines in the same facility/plant is continuously matched to the cases from the case-base for predicting the incipient faults-this local processing takes a much shorter time of a few seconds. The case-base is updated regularly (in the time scale of a few hours) on the cloud to include new cases of failure, and these case-base updates are pushed from CloudView to the local nodes. Experimental measurements show that fault predictions can be done in real-time (on a timescale of seconds) at the local nodes and massive machine data analysis for case-base creation and updating can be done on a timescale of minutes in the cloud. Our approach, in addition to being the first reported use of the cloud architecture for maintenance data storage, processing and analysis, also evaluates several possible cloud-based architectures that leverage the advantages of the parallel computing capabilities of the cloud to make local decisions with global information efficiently, while avoiding potential data bottlenecks that can occur in getting the maintenance data in and out of the cloud.