.
Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains.
This study presents a comprehensive analysis of hydrodynamic forces acting on particles in a three-phase (gas-liquid-solid) mixing tank using a coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) approach. A baffled cylindrical tank equipped with a Pitched Blade Turbine (PBT45) impeller is simulated under both upward and downward pumping directions over a range of speeds (300-800 rpm). Key particle-fluid forces - including drag, lift, pressure gradient, virtual mass, and flow-induced torque - are modeled to assess their individual and collective impacts on particle suspension and mixing dynamics. The simulations capture temporal evolution, spatial distributions, and time-averaged behavior of forces, providing insight into flow regime transitions and solid dispersion characteristics. Experimental validation is performed using both torque measurements and Electrical Resistance Tomography (ERT), showing good agreement between CFD-DEM predictions and experimental observations. Results indicate that pressure gradient and drag forces dominate particle-fluid momentum exchange, while virtual mass forces play secondary but directionally significant roles. Lift force and fluid-generated torque exhibit minor contributions across most operating conditions. Furthermore, comparison of impeller pumping directions reveals that upward pumping facilitates early suspension but shows diminishing force effectiveness at higher speeds, whereas downward pumping supports sustained force growth and more efficient suspension at high rotational speeds. These findings establish a quantitative and qualitative framework for comparing all major hydrodynamic forces in stirred tanks, offering both fundamental insight and practical recommendations for optimizing multiphase mixing simulations.
Pharmacologically active compounds (PhACs) are designed to help diagnose, treat, or prevent ailments and diseases; however, the majority of PhACs are not completely metabolizable by target organisms. Consequently, they are released into the aquatic environment from manufacturers as well as users, and without discharge guidelines and regulations, end up in the environment. While there are limited studies to support their direct impacts on human health at current environmental concentrations, there is an emerging concern stemming from studies on model aquatic organisms inciting negative ecological responses. Despite these avalanches of scientific evidence, there are regulatory and management gaps in removing or reducing the amount and rate at which pharmaceutical compounds enter natural water sources. This review identifies key knowledge and regulatory gaps associated with the presence of PhACs in the aquatic environment and proposes a management model for effective control at the source, consumption, and wastewater treatment levels. By synthesizing current evidence and highlighting the limitations of existing regulatory approaches, this paper underscores the need for more proactive and integrated management strategies capable of addressing both known and emerging challenges.
In this paper we develop a data-driven approach for supervisory control of discrete-event systems (DES). We consider a setup in which models of DES to be controlled are unknown, but a set of data concerning the behaviors of DES is available. We propose a new concept of data-informativity, which captures the notion that the available data set contains sufficient information such that a valid supervisor may be constructed for a family of DES models that all can generate the data set. We then characterize data-informativity with a necessary and sufficient condition, based on which we design an algorithm for its verification.
With the rise in global disasters, improving humanitarian supply chains and evacuation planning is essential for saving lives and delivering help quickly and fairly. This study proposes a model that integrates facility location, relief item distribution, and evacuation operations while accounting for critical social parameters such as demographic vulnerability and regional accessibility in affected areas. The inter-shelter collaboration logistics strategy is incorporated into the framework to address challenges in optimizing resource allocation and minimizing disruptions caused by blocked roads and uncertain demands. This research also develops a data-driven two-stage distributionally robust optimization (DRO) model, employing the worst-case mean-conditional value-at-risk criterion to ensure robustness against extreme scenarios. The model’s performance is assessed through out-of-sample analysis, demonstrating the DRO model’s enhanced robustness and effectiveness compared to the traditional two-stage stochastic programming model. The model is applied to the real case of the Fort McMurray wildfire in Alberta, Canada, to validate its practical applicability in disaster management. The results emphasize that prioritizing relief items, addressing social factors, and employing the inter-shelter collaboration strategy together improve evacuation efficiency and enhance resilience in disaster management, with the inter-shelter collaboration strategy contributing, for example, to approximately a 40% reduction in the unmet demand for a critical item.
