A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data. by Benoîte de Saporta, Aymar Thierry d'Argenlieu, Régis Sabbadin, Alice Cleynen

“…This method can also be adapted to other diseases.…”
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Measuring Component Importance for Network System Using Cellular Automata by Li He, Qiyan Cao, Fengjun Shang

“…This paper concentrates on the component importance measure of a network whose arc failure rates are not deterministic and imprecise ones. Conventionally, a computing method of component importance and a measure method of reliability stability are proposed. …”
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Improved Multiverse Optimization Algorithm for Fuzzy Flexible Job-Shop Scheduling Problem by Jin-Cheng Fang, A-Feng Zeng, Shao-Feng Zheng, Wen-Di Zhao, Xu He

“…An improved multiverse optimization (IMVO) algorithm is proposed herein for the fuzzy flexible job-shop scheduling problem pertaining to the non-deterministic polynomial-time hard (NP-hard) problem. …”
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A Comprehensive Review on the Integration of Renewable Energy Through Advanced Planning and Optimization Techniques by Carlos Barrera-Singaña, María Paz Comech, Hugo Arcos

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Multiobjective Transmission Network Planning considering the Uncertainty and Correlation of Wind Power by Yuan Hu, Zhaohong Bie, Yanling Lin, Guangtao Ning, Mingfan Chen, Yujie Gao

“…In order to consider the uncertainty and correlation of wind power in multiobjective transmission network expansion planning (TNEP), this paper presents an extended point-estimation method to calculate the probabilistic power flow, based on which the correlative power outputs of wind farm are sampled and the uncertain multiobjective transmission network planning model is transformed into a solvable deterministic model. …”
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FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION by Narkez Boranbayeva, Batyr Orazbayev, Leila Rzayeva, Zhalal Karabayev, Murat Alibek, Baktygul Assanova

“…The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. …”
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Data-driven identification and analysis of passenger riding paths in megacity metro system by Lianghui Xie, Zhenji Zhang, Robin Qiu, Daqing Gong

“…Design/methodology/approach – The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. …”
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Multi-objective optimization of residential energy-saving design based on fuzzy multi-criteria decision-making behavior model by Suzhen Pei, Gaoxiang Chen, Jian Yao, Yichen Dang

“…Compared to a deterministic baseline model assuming fixed occupant behavior, the proposed method achieves an 8.95 % reduction in per-unit-area energy consumption (from 32.5 to 29.6 kWh/m2), a 63 % decrease in inter-unit energy differences (from 510 to 189 kWh), and a marginal increase of about 2 % in annual discomfort hours (from 286 to 292 h). …”
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Resilience driven EV coordination in multiple microgrids using distributed deep reinforcement learning by Yuxin Wu, Ting Cai, Xiaoli Li

“…Then, the coordination problem of each EV is formulated as a partially observable Markov decision process, and an attention-based distributed multi-agent deep deterministic policy gradient method, namely AD-MADDPG, is proposed to learn the optimal strategies. …”
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Network control theory uncovers aberrant connectome controllability in trigeminal neuralgia by Tiantian Chu, Juan Peng, Feng Gao, Fei Xiong, Ye Tu

“…Structural connectomes were constructed using deterministic tractography and parcellated with the Brainnetome atlas. …”
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Multi-user joint task offloading and resource allocation based on mobile edge computing in mining scenarios by Siqi Li, Weidong Li, Wanbo Zheng, Yunni Xia, Kunyin Guo, Qinglan Peng, Xu Li, Jiaxin Ren

“…To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). …”
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Advanced predictive machine and deep learning models for round-ended CFST column by Feng Shen, Ishan Jha, Haytham F. Isleem, Walaa J.K. Almoghayer, Mohammad Khishe, Mohamed Kamel Elshaarawy

“…Comparison with 10 analytical models demonstrates that these traditional methods, though deterministic, struggle to capture the nonlinear interactions inherent in CFST columns, thus yielding lower accuracy and higher variability. …”
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Exploring Signature‐Based Model Calibration for Streamflow Prediction in Ungauged Basins by Marco Dal Molin, Dmitri Kavetski, Carlo Albert, Fabrizio Fenicia

“…In this study, the switch from observed to regionalized signatures increases predictive streamflow uncertainty by 38% and worsens the (deterministic) fit to observations by 17% (in terms of Nash‐Sutcliffe efficiency). …”
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Multi-UAV Path Planning for Air-Ground Relay Communication Based on Mix-Greedy MAPPO Algorithm by Yiquan Wang, Yan Cui, Yu Yang, Zhaodong Li, Xing Cui

“…In this paper, a Multi-Agent Reinforcement Learning (MARL) method based on the ε-greedy strategy and multi-agent proximal policy optimization (MAPPO) algorithm is proposed to address the local optimization problem, improving the communication efficiency and task execution capability of UAV cluster control. …”
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Stochastic simulation of soil moisture dynamics in farmland in the eastern region of the Songnen Plain. by Bo Meng, Jin Wang, Fanxiang Meng, Ennan Zheng, Tianxiao Li, Bowen Tang

“…Soil moisture is the core of the hydrological cycle in agroecosystems, and most of the studies on soil moisture dynamics modeling adopt deterministic research methods, which are not well suited to study the hydrological processes in agricultural fields under changing conditions. …”
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Wind speed prediction for trains on bridges using enhanced variational mode decomposition assisted feature extraction and physical auxiliary mechanism by Zhilan Zhu, Yuan Jiang, Haicui Wang, Shuoyu Liu

“…Hence, the proposed method stands as a viable and efficient alternative for forecasting the short-term wind speed.…”
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Interval Uncertainty Analysis for Wheel–Rail Contact Load Identification Based on First-Order Polynomial Chaos Expansion by Shengwen Yin, Haotian Xiao, Lei Cao

“…Traditional methods for identifying wheel–rail contact loads are based on deterministic models, in which the uncertainties such as material inhomogeneity and geometric tolerance are not considered. …”
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Generative adversarial models for extreme geospatial downscaling by Guiye Li, Guofeng Cao

“…Given an input, the method can produce a multitude of plausible high-resolution samples instead of one single deterministic result. …”
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Artificial Intelligence in UAV Flight Controls: Deep Reinforcement Learning Based Altitude-Hold Strategies for Fixed-Wing UAVs by Hasan Raza Khanzada, Adnan Maqsood, Abdul Basit

“…The deep deterministic policy gradient (DDPG) method was chosen to suit environments with continuous action spaces. …”
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Research on Long-Term Scheduling Optimization of Water–Wind–Solar Multi-Energy Complementary System Based on DDPG by Zixing Wan, Wenwu Li, Mu He, Taotao Zhang, Shengzhe Chen, Weiwei Guan, Xiaojun Hua, Shang Zheng

“…To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). …”
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