The proliferation of last-mile autonomous delivery fleets requires robust, scalable, and communication-efficient multi-agent coordination frameworks to safely navigate dense urban environments. Traditional multi-agent pathfinding approaches frequently scale poorly under high agent density or suffer severe performance degradation during sudden communication dropouts. To resolve these operational challenges, this paper presents a novel distributed hybrid coordination framework that integrates macroscopic consensus-based task allocation with localized, dynamic conflict resolution strategies. By implementing a decentralized token-passing auction model alongside asynchronous dynamic window path updates, the system guarantees conflict-free trajectories without relying on a persistent, centralized server. Extensive software co-simulations and physical field trials demonstrate that the proposed framework achieves a 22.4% reduction in path conflict frequency and a 16.8% improvement in fleet resource utilization compared to baseline prioritized planning models. These results prove that the system is highly resilient and viable for high-density, real-world autonomous logistics infrastructures.
Thomas Fischer, Anna Schmidt· International Journal of Int...· 0 citations
The sphere of agriculture is currently experiencing an enormous change fueled by the incorporation of the Artificial Intelligence (AI), Internet of Things (IoT), big data analytics, and new sensing technologies. The challenges to farming by the traditional practices usually include; poor use of resources, unpredictable weather, pests, and poor production. AI-based smart farming will provide data-driven, demonstration-free, and predictive solutions that will improve crop yielding, optimize resource use and enable sustainable and agricultural development. Within this paper, a detailed analysis of AI-based smart farming technologies to improve the crop condition is provided, including smart decision-making, precision agriculture, and real-time detection. The suggested framework is an integration of machine learning algorithms, computer vision, remote sensing, and internet of things with devices that will be used to touch soil, weather condition, and crop growth phases and pests. The prediction of yields, detection of diseases and the optimization of irrigation are reviewed using various AI methodologies including supervised learning, deep learning, and reinforcement learning. A comparative analysis shows that AI-based methods outperform the traditional farming methods in the aspects of productivity, economic efficiency, and environmental sustainability. The implementation challenges and scalability issues, as well as research directions are also addressed in the study. The results show that AI-combinations with smart farming can transform the agriculture sector as the application can lead to better quality of crops, higher yield and sustaining food security the world over due to climatic change and population explosion.
Thomas Fischer, Anna Schmidt· International Journal of Mod...· 0 citations
The rapid growth of social media has transformed political communication by decentralizing information flows and replacing traditional gatekeeping with algorithm-driven content distribution. This shift has significantly influenced democratic participation, opinion formation, electoral mobilization, polarization, and information integrity. This study examines how political narratives are created and amplified through user-generated content, algorithmic curation, and network dynamics. Using a hybrid methodology—combining content analysis, sentiment modeling, network centrality measures, and engagement metrics—the research proposes computational indicators such as the Narrative Amplification Index (NAI), Sentiment Dominance Ratio (SDR), and Polarization Coefficient (PC) to measure narrative influence. Analysis of social media posts during a major electoral cycle reveals that algorithmic amplification strengthens ideological homogeneity and increases cross-group polarization. Approximately 68% of highly engaged political posts contained emotionally charged framing, highlighting the role of affective language in narrative virality. Coordinated networks also contributed significantly to rapid narrative diffusion. The findings demonstrate that social media platforms actively shape political discourse through engagement-driven algorithms, prioritizing emotionally intense and polarizing content. The study provides computational insights into digital political ecosystems and offers implications for policymakers and platform regulators seeking to balance free expression with information integrity
Thomas Fischer, Anna Schmidt· International Journal of Eme...· 0 citations
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