Flooded roadways threaten mobility and public safety, underscoring the need for high-resolution, road-specific flood prediction tools. This study developed a deep learning framework to predict flood depth and velocity on transportation networks using geomorphic and hydrologic features. A UNet-based Convolutional (UNet Conv) model and a UNet Convolutional Long Short-Term Memory (UNet ConvLSTM) model were implemented for flood depth and velocity, respectively. By incorporating roadway-focused weighted loss functions, the models prioritized accuracy on road pixels and achieved strong performance (Depth: R2 ≈ 0.89, RMSE ≈ 0.07 m; Velocity: R2 ≈ 0.93, RMSE ≈ 0.08 m/s). The framework generalized well to unseen storm conditions (R2 > 0.75). Unlike conventional hydrologic and hydraulic models, this method eliminates the need for time- and resource-intensive modeling setups, thereby facilitating rapid flood prediction using readily available data. These results demonstrate the potential of road-focused deep learning to support flood forecasting, transportation resilience planning, and emergency response.
The increasing demand for durable and efficient corrosion inhibitors has intensified interest in macrocyclic ligands as an emerging class of tunable corrosion inhibitors. Several salient features, including macrocyclic effect, host-guest recognition, preorganized multidentate coordination, extensive surface coverage, and metal-ion complexation within a single molecular framework, make them an attractive and effective class of inhibitors. These properties also make them different from traditional corrosion inhibitors. The present review provides broad coverage of phthalocyanines, crown ethers, porphyrins, azamacrocycles, and calixarenes, with particular focus on their structure-performance relationships, coordination mechanisms, adsorption, and the formation of corrosion-protective films on different substrates, including steel, iron, aluminum alloys, copper, and magnesium in acidic, alkaline, and saline environments. Macrocyclic inhibitors exhibit a broad range of inhibition efficiencies depending on their molecular structure, metallic substrate, and experimental conditions. This review also highlights the roles of donor atoms, molecular geometry, ring or cavity size, metal coordination, substituents, and solubility in the adsorption, coordination, and corrosion inhibition of macrocyclic ligands. This article also discusses their challenges related to poor solubility, synthetic complexity, scalability, environmental consideration, and limited industrial-scale validation under realistic conditions. Lastly, research gaps in macrocyclic-based corrosion inhibition have also been presented with outlines, enabling effective and sustainable design of macrocycle-based corrosion inhibitors.
Micron-sized, densely packed spherical aggregates of aluminum with a narrow particle size distribution were prepared by Emulsion-Assisted Milling (EAM). Combustion of this powder was compared to that of similarly sized atomized aluminum. Single particles of each powder were fed into an air-acetylene flame. The ignited particles continued to burn in room air. Each burning particle produced an optical emission pulse. Such pulses were measured using two photomultipliers filtered at 700 and 800 nm. The measured signals were processed to recover the pulse emission intensities, durations, and color temperatures for each particle. A statistically significant dataset of descriptors for the optical emission pulses produced by burning particles was obtained and processed for each powder. In addition, burned-out particles were collected and examined using electron microscopy. The results show that the EAM-prepared aluminum powder particles generate a stronger emission than atomized particles. The particle burn times are comparable for both powders. The optical signatures of the burning particles are successfully represented as a sum of contributions from both the particle surface and the standoff flame. In both cases, the flame is assumed to be at the boiling point of alumina. The particle surface temperature appears to be lower for the milled powder particles. Despite that, the emission from the standoff flame is markedly stronger for the EAM-prepared aluminum. Consistent with their lower surface temperatures, the EAM-prepared aluminum particles do not coalesce into single droplets during combustion, retaining at least partially their initial morphology. This altered surface morphology is likely responsible for the observed differences in their combustion characteristics.
Abstract There has been recent interest in smart human–building interaction (HBI) interfaces to improve how occupants interact with building systems. Intelligent facility management (FM) utilizes HBI principles to transform static infrastructures into dynamic, human-centric environments, where facility managers need to interpret live Internet-of-things (IoT) sensor data, consult procedures, and execute safe control actions for various building systems. These tasks are fragmented across dashboards, manuals, and control panels, increasing cognitive load and slowing down decision-making. To address these challenges, this study proposes the multiagent extended-reality and sensing-aware task-level reasoning orchestrator (MAESTRO). MAESTRO is a multimodal agentic GenAI assistant comprised of four agents that support procedural queries, telemetry interpretation, and supervised device actuation in a single HBI loop. MAESTRO integrates a large action model (LAM)-based system with retrieval-augmented reasoning interfaced in an extended reality (XR) environment and mediated by a Model Context Protocol. It uses an orchestrator, which is the coordination component that interprets each user request and decides which agent capability should handle the request, while enforcing supervised and schema validated execution for sensing and actuation. The orchestrator routes each request to the appropriate agents including a domain-adapted FM language agent trained on manuals, logs, and work orders; an IoT control agent that generates schema validated tool calls to sensors and actuators; and a vision language agent for image-based queries. MAESTRO was evaluated on various HBI testing scenarios covering safety inspection, system control, troubleshooting, and procedural guidance. The results show improved language modeling and answer quality after adaptation, a valid tool-call rate of 91%, device-execution success of 89%, telemetry match rate of 81%, and an overall end-to-end task success rate exceeding 84%. This paper shows the potential of integrating fine-tuned LAM-based agents with structured IoT-enabled middleware and XR-based HBI systems, demonstrating how foundation-model reasoning and actuation can support more intelligent and adaptive HBI workflows.
Spherical composite boron powders with different particle sizes and consisting of densely aggregated nano-sized primary particles were prepared using emulsion-assisted milling. They were compared to commercial boron consisting of irregularly shaped, finer, and less densely packed primary particle aggregates. Powder particles were injected into an oxygen-acetylene burner. Their combustion was characterized using recorded optical emission pulses. For each particle pulse, characteristic brightness, duration, and color temperature were determined. Measured color temperatures of the burning commercial boron particles were close to the melting point of boron. The composite milled powders exhibited higher combustion temperatures and greater emission intensities than commercial boron, despite comparable burn times. Characteristic BO2 emission was observed and used for additional temperature measurements. Recovered combustion products did not coalesce into single molten droplets, retaining, at least partially, their initial aggregate structure. This was attributed to the production of volatile suboxides (e.g., BO2) at the surfaces of individual primary particles. Staged combustion was observed at high temperatures and distinctly after ignition. The present results emphasize the importance of vapor-phase reactions and challenge previous models that assigned the first combustion stage solely to the removal of molten B2O3. Bulk burn rates of boron particles were consistent with those estimated using the reaction kinetics quantified from thermoanalytical experiments on boron oxidation at much lower temperatures.
