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  1. 1041
    “…We introduce an alternative approach that optimally schedules practice with a computational model of spacing in tandem with microeconomic principles. …”
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    Optimizing practice scheduling requires quantitative tracking of individual item performance
  2. 1042
    por Lee, Wonseok, Kim, Taehong, Kim, Taejoon
    Publicado 2020
    “…In this paper, a novel distributed scheduling scheme for an ad-hoc network is proposed. …”
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    Distributed Node Scheduling with Adjustable Weight Factor for Ad-hoc Networks
  3. 1043
    “…Reliability characteristics were used to develop predictive schedules. Schedules were assessed using the criteria of solution and quality robustness. …”
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    Predictive Maintenance Scheduling with Failure Rate Described by Truncated Normal Distribution
  4. 1044
    “…In this paper, a novel dynamic time division multiple access (TDMA) scheduling strategy is proposed for MANETs. Firstly, a service priority-based dynamic TDMA scheduling (SP-DS) algorithm is presented, which introduces the service priority as a reference factor for slot allocation and fully considers the transmission throughput and end-to-end delay performance. …”
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    A Dynamic TDMA Scheduling Strategy for MANETs Based on Service Priority
  5. 1045
    “…First of all, we propose a metric to compare the quality of semantic features, and utilize appropriate features as input for a semantic detection network (SDN) with adequate complexity in order to generate meaningful semantic features for videos. Then, we apply a scheduled sampling strategy that gradually transfers the training phase from a teacher-guided manner toward a more self-teaching manner. …”
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    A Semantics-Assisted Video Captioning Model Trained With Scheduled Sampling
  6. 1046
    “…It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. …”
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    Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
  7. 1047
    por Park, Mingyu, Paek, Jeongyeup
    Publicado 2021
    “…To address this challenge, we propose SCoRe, an on-demand scheme for joint scheduling of command and responses on multihop low-power wireless networks to improve both reliability and latency simultaneously at runtime. …”
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    On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks †
  8. 1048
    por Li, Zhiyuan, Peng, Ershuai
    Publicado 2021
    “…To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. …”
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    Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking †
  9. 1049
    “…However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). …”
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    Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing
  10. 1050
    “…BACKGROUND: Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial. …”
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    Phase I dose-escalation oncology trials with sequential multiple schedules
  11. 1051
    “…This data article presents a flow shop scheduling problem in which machines are not available during the whole planning horizon and the periods of unavailability are due to random faults. …”
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    Dataset of metaheuristics for the flow shop scheduling problem with maintenance activities integrated
  12. 1052
    “…In mice with platelet-derived-growth-factor-driven glioblastoma, the model-optimized treatment schedule increased the survival of the animals. For standard radiation fractionation in patients, the model predicts that chemotherapy may be optimally administered about an hour before the application of radiation. …”
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    Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma
  13. 1053
    por Müssig, Karsten
    Publicado 2021
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    Stark schwankende Blutglukosespiegel begünstigen wohl schweren COVID-19-Verlauf
  14. 1054
    por Kepka, Lucyna, Socha, Joanna
    Publicado 2021
    “…There is a similar problem with moderate hypofractionation in radical RT for locally advanced NSCLC, and more evidence needs to be gathered regarding the safety of such schedules, especially when used in combination with CHT. …”
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    Dose and fractionation schedules in radiotherapy for non-small cell lung cancer
  15. 1055
    “…This decision is made taking the status of the network into account. This scheduling algorithm was implemented as an extension to the Kubernetes default scheduler, and compared with existing proposals in the literature. …”
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    Quality of Service Provision in Fog Computing: Network-Aware Scheduling of Containers
  16. 1056
    “…Secondly, by formalizing the notion of the therapeutic target being maintenance rather than eradication, we show that in the short term, optimal chemotherapy scheduling consists mainly of a drug applied at a low dose. …”
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    Angiogenesis and chemotherapy resistance: optimizing chemotherapy scheduling using mathematical modeling
  17. 1057
    por Guerriero, Francesca, Guido, Rosita
    Publicado 2021
    “…In this paper, we propose optimization models to address flexible staff scheduling problems and some main issues arising from efficient workforce management during the Covid-19 pandemic. …”
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    Modeling a flexible staff scheduling problem in the Era of Covid-19
  18. 1058
    por Müller, Thomas
    Publicado 2021
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    Extreme Müdigkeit weist auf schweren COVID-19-Verlauf hin
  19. 1059
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    Impact of COVID-19 on Patients Undergoing Scheduled Operations for Venous Disease
  20. 1060
    “…The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. …”
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    Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
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