Adaptive Consensus Protocols for High-Throughput Decentralized Sharding in Heterogeneous Edge Networks

Sophia Chen, Liam O'Connor

Decentralized distributed ledgers deployed across heterogeneous edge topologies frequently suffer from transaction throughput degradation caused by asynchronous validator latency and uncoordinated cross-shard communication. In this paper, we formulate an Adaptive State-Partitioned Consensus (ASPC) protocol that dynamically recalibrates shard clustering based on peer link quality and localized compute density. We establish formal safety and liveness bounds under non-synchronous network bounds and demonstrate a 3.8x throughput speedup over standard PBFT variants under peak stress conditions across a 250-node geographic testbed.

Hierarchical Graph Neural Networks for Zero-Shot Protein-Ligand Complex Binding Affinity Prediction

Victoria Sterling, Rajesh Patel

Accurately predicting binding affinities of drug-like small molecules to target proteins in silico is fundamental to modern structure-based therapeutic discovery. Here, we present EquiAffinity-GNN, an SE(3)-equivariant hierarchical graph architecture that unifies atomic-level geometry with pocket-level residue interactions. Benchmarked against the standard PDBbind v2020 refined core set, our framework achieves a Pearson correlation of R = 0.864 and RMSE = 1.12 kcal/mol, significantly outperforming conventional docking force fields and flat GNN baselines.

A Decade of Transformer Architectures in Computational Vision: Comprehensive Survey and Future Horizons

Elena Rostova, Marcus Vance

Since the introduction of attention mechanisms to computer vision, transformer-based neural architectures have redefined the state of the art in image classification, semantic segmentation, 3D point cloud processing, and generative synthesis. This survey systematically reviews over 180 key contributions over the past ten years, categorizing architectural paradigms by attention formulation, tokenization mechanics, and compute efficiency trade-offs.

Field Deployment and Resiliency Validation of Zero-Trust Sensor Mesh Networks in Industrial IoT Facilities

Karl Müller

Critical industrial infrastructure requires uninterrupted telemetry and stringent anomaly detection against adversarial cyber-physical tampering. We detail the five-year field architecture and empirical security evaluation of an authenticated zero-trust mesh sensor deployment spanning an operational oil refinery comprising over 10,000 connected telemetry points.