Mathematical and Numerical Modeling of Information Dissemination in Mobile Networks
Bibliographic record
Abstract
This special issue covers a number of developing topics in mathematical and numerical modeling of information dissemination in mobile networks.The 15 research articles included in this special issue present original research outcomes and future evolutions of mathematics in mobility and networking.From communication mechanisms to mobile applications, the topics of this special issue are classified into three categories, namely, complex models, techniques, and applications.The first group of papers addresses issues in the area of information dissemination via mathematical modelling approaches.In the paper of "Efficient periodic broadcasting for mobile networks at small client receiving bandwidth and buffering space, " H.-F. Yu et al. introduced a new Fibonaccibroadcasting scheme (called FiB+) for video broadcasting and achieved smaller client buffering space than that of FiB under two-channel receiving bandwidth.In the paper entitled "Single-channel data broadcasting under small waiting latency, " H.-F. Yu proposes a single-channel broadcasting scheme for video-on-demand services.By partitioning a video into equal-sized segments, these classified segments have been transferred over a single channel according to a predefined arrangement to yield short waiting time of data broadcasting.In the paper "A mutual-evaluation genetic algorithm for numerical and routing optimization, " C.-H. Lin and J.-D.He present a mutual-evaluation genetic algorithm (MEGA) to find optimal flow-allocation strategies for multipath-routing problems.In the paper entitled "Minimum-cost QoS-constrained deployment and routing policies for wireless relay networks, " F.-Y.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".