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Optimized sowing rate and fertilization regulate photosynthetic characteristics, grain yield and quality of winter wheat
An N-terminal amphipathic helix governs activity and conformational dynamics of Nramp metal transporters
Integrated solar PV energy management and adaptive control of IPMSM drives for lightweight electric trikes
Glucose-6-phosphate dehydrogenase variants modify 3D genomic organization to suppress maladaptive gene expression and vascular disease
Healthcare provider and organizational readiness of mobile-based app implementation for disease classification in northwest Ethiopia: a cross-sectional study
Azotobacter vinelandii glutaredoxin D delivers the core [Fe2S2] cluster to nitrogenase cofactor scaffold protein NifU
Effects of real-time and potential traffic congestion on network throughput
Abstract To reduce traffic congestion and improve network throughput, this study focuses on the dynamic routing process by constructing a unified path-cost function that integrates both a real-time traffic congestion index (derived from instantaneous node queue lengths) and a potential traffic congestion index (estimated from the expected number of future paths traversing each node based on active routing decisions). Through extensive simulations on Barabási–Albert scale-free network and Erdös–Rényi random network, we systematically investigate how these two types of congestion information affect network throughput. The results reveal that real-time traffic congestion is more significant than potential traffic congestion in improving network throughput. By comparison with five routing algorithms, it is found that the routing strategy considering both real-time and potential traffic congestion are more efficient. The main contribution of this work is to reveal the impacts of real-time and potential congestion on network throughput, clearly establishing their primary-secondary relationship in throughput control, and providing empirical guidelines and parameter-tuning recommendations for hybrid routing designs that combine immediate responsiveness with predictive awareness.