This is the MATLAB source code for a stochastic agent-based model of CD8+ T cell responses following vaccination. The model simulates T cell activation, proliferation, contraction, and differentiation into effector and memory populations. Cell division is regulated by intracellular c-Myc dynamics, and the model includes asymmetric inheritance of c-Myc between daughter cells. A lineage tracking module is also added to follow the descendants and division histories of individual simulated T cell families.

Sustainable aviation fuel (SAF) plays a central role in decarbonizing the aviation sector. Brazil is positioned to become a leader in SAF production and consumption from sugarcane-based ethanol. However, integrating SAF into the Brazilian sugarcane industry is challenging due to high production costs, an undeveloped SAF supply chain, and ethanol competition between ground and aviation transportation. The present study uses mixed-integer linear optimization to design SAF supply chains in Brazil under various blend requirements and design scenarios. Three sugarcane mills in São Paulo are identified for strategic SAF investments, with a recommended premium of 2.6 R$ L⁻¹ (0.5 US$ L⁻¹). Furthermore, SAF production increases net CO₂ emissions by up to 2.7 Mt year⁻¹ (relative to a 0% baseline) due to ethanol displacement. Sensitivity analysis reveals SAF conversion and emission targets for net-zero system emissions. These results provide several key insights to guide SAF supply chain growth,... [more]

Economic and environmental analysis of isopropanol-acetone-hydrogen based chemical heat pump (IAH-CHP) were studied at the endothermic reaction temperatures (heat input) of 100°C-200°C at gross temperature lifts of 50°C, 75°C and 100°C and heat output capacity of 500 kW.
Attached files are the following:
Equipment cost correlations along with key parameter values used in economic analysis.
List of ecoinvent processes used in Life cycle assessment studies.
Sample economic calculation file for 130°C/205°C case.
Optimization results - decision variable values along with cost details for all the cases studied.

This supplementary material provides supporting methodological and numerical information for the study of uncertainty-aware fleet planning for heavy-duty freight. It expands on the construction of the case study, including the truck technologies, route characteristics, freight classes, operating scenarios, and economic and environmental assumptions used in the stochastic optimization model.
The document also provides additional formulation details, data tables, computational settings, and supporting analyses needed to interpret and reproduce the results. Together, these materials complement the main manuscript by documenting assumptions and calculations that could not be included in the article because of space limitations.

Supporting Information document to submission for Foundations of Computer Aided Process Operations - Chemical Process Control Conference 2027

Recent research has introduced large language model (LLM)-based chatbots to help practitioners interpret solutions of optimization models. Existing chatbots primarily support the interpretation of a solution obtained from a single solve. In rolling horizon optimization, however, practitioners need to understand how and why solutions change across successive solves as the planning window advances and changes occur in model data and/or structure. We present an LLM-based agentic framework for this comparison task, works for time-indexed linear programming (LP) and mixed-integer linear programming (MILP) models represented in Pyomo. Four deterministic analytical modules detect model changes, quantify solution differences, assess whether prior decisions remain feasible under the updated model, and and links differences between successive plans to changes in model parameters and structure. LLM agents interpret user questions, select the corresponding module, and synthesize its numerical resu... [more]

This document presents the objective functions for the deterministic case study and the reformulated stochastic model, along with the tables and data used in the modeling process.