Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data

To understand complex system dynamics in dairy farming, it is essential to use modeling tools that capture farm heterogeneity, social interactions, and cumulative environmental impacts. This study proposes an agent-based modeling (ABM) framework to simulate nitrogen management and the adoption of low-emission fertilizer across 295 Irish dairy farms over a 15-year period. Using empirical data, the model represents farm communication through a social network, capturing peer influence and discussion group dynamics, where adoption probabilities are driven by social contagion, farm-scale characteristics, and policy interventions such as subsidies and carbon taxes. The framework estimates sectoral greenhouse gas emissions, cumulative abatement, and private-social cost trade-offs, using Monte Carlo simulation and sensitivity analysis to quantify uncertainty. The model shows strong agreement with observed adoption trajectories ($R^2 = 0.979$, RMSE = 0.0274) and is validated against empirical data using a Kolmogorov-Smirnov test (D = 0.2407, p < 0.001), indicating its ability to reproduce structural patterns in adoption behavior. Adoption dynamics are further characterized using a logistic diffusion model consistent with Rogers' innovation diffusion theory, capturing progression from early adoption to a saturation level of approximately 91%. By framing decarbonization as a socio-technical diffusion process rather than a purely economic optimization problem, this study provides an in silico policy laboratory for evaluating the robustness and diffusion speed of climate mitigation strategies prior to implementation.

Graph-Spectral Fusion of Wavelet Packets and Higher-Order Statistics for Anomaly Detection in Industrial IoT Networks

Industrial Internet of Things (IIoT) networks demand reliable anomaly detection under harsh wireless conditions, yet most detectors fail on four fronts: hostile fading, stealthy non-Gaussian faults, discarded spatial structure, or constrained edge hardware. We propose Graph WPT+HOS, a classical label-free detector that fuses three complementary views: the Graph Fourier Transform (GFT) for spatial inconsistency, the Wavelet Packet Transform (WPT) for transient time-frequency localization, and Higher-Order Statistics (HOS) for non-Gaussian shape. The fused features are scored by a Mahalanobis distance with Ledoit-Wolf shrinkage and converted to alarms by a one-sided CUSUM. The pipeline is asymptotically optimal at the decision stage, requires no labeled anomalies, and runs on ARM-class edge hardware without GPU acceleration. Across six baselines and four domain-shift regimes under Rayleigh fading, Graph WPT+HOS attains the highest ROC-AUC and PR-AUC and reduces CUSUM detection latency.