A heat transfer model that can predict the temperature distribution in moving flexible composite materials (webs) for various heating/cooling conditions is developed in this paper. Heat transfer processes are widely employed in roll-to-roll (R2R) machines that are used to perform processing operations, such as printing, coating, embossing, and lamination, on a moving flexible material. The goal is to efficiently transport the webs over heating/cooling rollers and ovens within such processes. One of the key controlled variables in R2R transport is web tension. When webs are heated or cooled during transport, the temperature distribution in the web causes changes in the mechanical and physical material properties and induces thermal strain. Tension behavior is affected by these changes and thermal strain. To determine thermal strain and material property changes, one requires the distribution of temperature in moving webs. A multilayer heat transfer model for composite webs is developed in this paper. Based on this model, temperature distribution in the moving web is obtained for the web transported on a heat transfer roller and in a web span between two adjacent rollers. Boundary conditions that reflect many types of heating/cooling of webs are considered and discussed. Thermal contact resistance between the moving web and heat transfer roller surfaces is considered in the derivation of the heat transfer model. Model simulations are conducted for a section of a production R2R coating and fusion process line, and temperature data from these simulations are compared with measured data obtained at key locations within the process line. In addition to determining thermal strain in moving webs, the model is valuable in the design of heating/cooling sources required to obtain a certain desired temperature at a specific location within the process line. Further, the model can be used in determining temperature dependent parameters and the selection of operating conditions such as web speed.

Personalization is a core capability across consumer technologies, streaming, shopping, wearables, and voice, yet it remains challenged by sparse interactions, fast content churn, and heterogeneous textual signals. We present RecMind, an LLM-enhanced graph recommender that treats the language model as a preference prior rather than a monolithic ranker. A frozen LLM equipped with lightweight adapters produces text-conditioned user/item embeddings from titles, attributes, and reviews; a LightGCN backbone learns collaborative embeddings from the user-item graph. We align the two views with a symmetric contrastive objective and fuse them via intra-layer gating, allowing language to dominate in cold/long-tail regimes and graph structure to stabilize rankings elsewhere. On Yelp and Amazon-Electronics, RecMind attains the best results on all eight reported metrics, with relative improvements up to +4.53\% (Recall@40) and +4.01\% (NDCG@40) over strong baselines. Ablations confirm both the necessity of cross-view alignment and the advantage of gating over late fusion and LLM-only variants.