The growing adoption of Large Language Models and autonomous agent frameworks has brought to light a fundamental tension in enterprise software design. On one side lie decades of carefully engineered System APIs whose behaviour is deterministic, whose schemas are versioned and whose security boundaries are well understood. On the other side sit AI agents that require fluid, semantically rich tool interfaces, contextual descriptions of available operations, and the ability to discover capabilities at runtime without prior hardcoding. Closing this gap in a principled, production-safe way is the central problem this work addresses. We propose the Agent-Ready Architecture (ARA), a framework built around Anthropic’s Model Context Protocol (MCP). Rather than replacing existing System APIs, ARA introduces an MCP-compliant orchestration layer that wraps legacy endpoints, enriches them with natural-language tool descriptions and structured parameter schemas, and registers them in a semantic discovery registry that agents can query at runtime. Process APIs are re-architected as MCP Hosts, each managing its own set of MCP Servers so that multi-step business workflows can be invoked through a single, intent-driven interface. To evaluate this architecture, we introduce two primary metrics: Semantic Discovery Precision, which measures how accurately an agent selects the correct tool given a natural-language task description, and Tool-Invocation Latency, which captures the end-to-end cost of an agent-initiated operation. Our experiments on a six-service enterprise Kubernetes deployment show that ARA raises Semantic Discovery Precision from 59% to an average of 90% across five query categories, reduces multi-tool chain latency by 60.5%, and lowers agent hallucination rates by approximately 40% when the tool context grows beyond six concurrent instruments. A Context-Aware Service Mesh layer supplies the governance primitives, including token-budget enforcement and intent-based rate limiting, that are necessary to operate AI-driven request patterns safely at enterprise scale
At the edge of a coastal heathland near Cuxhaven in the Lower Saxony Wadden Sea National Park, atotal of 47 thalli of the foliose lichen Punctelia reddenda (Stirt.) Krog, previously unrecorded in Germany, were found on two oak trees. The local conditions of the site and occurring associated species are described in connection with the current distribution of the species in Western Europe. Finally, reference is made to the nature conservation significance.
The downstreaming of Rare Earth Elements (REE) in Indonesia has significant potential to support national industrialization but faces challenges in environmental management and community involvement. Communities in REE-producing areas generally lack sufficient understanding of the ecological impacts and economic opportunities related to downstreaming activities. This community service program aims to enhance local community capacity through education and innovation in environmental management and sustainable utilization of REE downstreaming potential. The program applied a qualitative approach through socialization, counseling, group discussions, and community mentoring in potential REE-producing areas. It was supported by policy analysis and participatory observation to assess community understanding and engagement. The community service activities resulted in improved public awareness of environmental impacts and REE waste management. Communities became more active in promoting environmentally friendly practices and initiated simple innovations to support sustainable downstreaming. The conclusion of this community service program shows that education and innovation are effective strategies to strengthen community roles as active partners in supporting sustainable REE downstreaming. This approach can serve as a collaborative model between government, industry, and communities to promote environmentally conscious development of strategic natural resources.
The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system, and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, the three mMPMS were installed at a full-scale pulverized coal-fired plant, PacifiCorp's Hunter 3. The systems were demonstrated over 20,000 hours at the plant during regular operation. Also, the sensor data was fed to the plant's advanced process control system to evaluate the corrosion control by the operation changes and utilized to understand the impacts of load cycling with different ramping up and down speeds. At the end of the project, the systems were converted to the permanent installation at the power plant to be used with the advanced process control system installed at the plant.
The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative's Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp's Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.
