{"id":"W4307874069","doi":"10.32604/cmc.2023.028597","title":"Analysis on D2D Heterogeneous Networks with State-Dependent Priority燭raffic","year":2022,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Government of Jiangsu Province","keywords":"Priority inheritance; Computer science; Priority queue; Priority ceiling protocol; Queueing theory; Queue; Correctness; Network packet; Computer network; Scheduling (production processes); Priority inversion; Real-time computing; Deadline-monotonic scheduling; Dynamic priority scheduling; Mathematical optimization; Algorithm; Round-robin scheduling; Rate-monotonic scheduling; Quality of service; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001805461,0.001787475,0.002984147,0.001202405,0.0007046778,0.001501534,0.001930427,0.0003943407,0.001302132],"category_scores_gemma":[0.0000232449,0.001917504,0.000424837,0.001500935,0.0002296404,0.0005961417,0.001508408,0.0007258367,0.0001535882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000906769,"about_ca_system_score_gemma":0.00008392674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001259189,"about_ca_topic_score_gemma":0.00009315067,"domain_scores_codex":[0.9906315,0.0009923173,0.002618606,0.002156904,0.001330554,0.002270103],"domain_scores_gemma":[0.9954125,0.0004971848,0.001151475,0.002067574,0.0003580185,0.0005132818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00102347,0.0002487664,0.0003133014,0.0001549753,0.001856991,0.0005573472,0.0006011752,0.9554054,0.02743581,0.0001098736,0.001903113,0.01038975],"study_design_scores_gemma":[0.01760653,0.003871641,0.009493972,0.001397919,0.003495518,0.0009333512,0.0003959152,0.6063874,0.3078359,0.0002420586,0.03533125,0.0130086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6389397,0.0002470608,0.3475683,0.00005709512,0.009001691,0.001726567,0.0003466309,0.00201956,0.00009332149],"genre_scores_gemma":[0.9814276,0.0001844151,0.01391748,0.0005181763,0.001658099,0.0003911432,0.001138915,0.0005183954,0.0002458515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.349018,"threshold_uncertainty_score":0.9996108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004609764940400173,"score_gpt":0.1865193322862308,"score_spread":0.1819095673458306,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}