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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Earthquake Decision-Making Tool for Humanitarian Logistics Network: An Application in Popayan, Colombia by Helmer Paz-Orozco, Irineu de Brito Junior, Mario Chong, Yesid Anacona-Mopan, Jhon Alexander Segura Dorado, Mariana Moyano

“…<i>Background</i>: This study presents a comprehensive methodology for enhancing humanitarian logistics planning and management in natural disasters, focusing on earthquakes. <i>Methods</i>: The innovative approach combines a deterministic mathematical model with a simulation model to address the problem from multiple perspectives, aiming to improve efficiency and equity in post-disaster supply distribution. …”
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Coupling Machine Learning and Physically Based Hydrological Models for Reservoir-Based Streamflow Forecasting by Benjun Jia, Wei Fang

“…Taking the Yalong River as an example, the main results are as follows: (1) Deep learning models (ConvLSTM and LSTM) show good performance in forecast precipitation correction and reservoir operation rule extraction, contributing to streamflow forecasting accuracy. (2) The proposed streamflow deterministic forecasting method has good forecasting performance with <i>NSE</i> above 0.83 for the following 1–5 days. (3) The GMM model, using upstream evolutionary forecasted streamflow, interval forecasted streamflow, and downstream forecasted streamflow as the input–output combination, has good probabilistic forecasting performance and can adequately characterize the “non-normality” and “heteroskedasticity” of forecasting uncertainty.…”
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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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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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A Novel Multiobjective Optimization Approach for EV Charging and Vehicle-to-Grid Scheduling Strategy by Muhammad Aurangzeb, Yifei Wang, Sheeraz Iqbal, Md Shafiullah, Sultan Alghamdi, Zahid Ullah

“…These enhancements make our method more effective, flexible, and capable of supporting the transition to a sustainable and efficient energy system.…”
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Research of the effectiveness of equity capital by chemical industry companies during the crisis by O. V. Berezhnaya, E. V. Berezhnaya

“…The purpose of the work is to assess the effectiveness of using their own sources of financing, typical for companies in the chemical industry, during the period of crisis and sanctions restrictions from 2019 to 2022.Materials and methods. Based on traditional methods of statistical analysis, financial analysis and classical deterministic models (in particular, return on equity capital), on the basis of information on the financial results and capital structure of organizations in the SPARK-Interfax system, the authors analyzed the dynamics of equity capital, its components in the chemical industry and its sub-sectors. …”
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Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning by Ce Feng, Parv Venkitasubramaniam

“…Through rigorous theoretical analysis and extensive experiments on benchmark datasets, we demonstrate that these methods significantly enhance the utility-privacy trade-off and computational efficiency in both ML-at-the-edge and FL systems. …”
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Fundamental properties and characteristics of flux distribution tallies using proper orthogonal decomposition by Kondo Ryoichi, Yamamoto Akio, Endo Tomohiro

“…This result indicates that the deterministic method might be more efficient for the snapshot calculation. …”
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Optimal Design of Interior Permanent Magnet Synchronous Motor Considering Various Sources of Uncertainty by Giacomo Guidotti, Dario Barri, Federico Soresini, Massimiliano Gobbi

“…Finally, the results of the robust optimization are compared with those of the deterministic optimization. Due to the small margin of improvement in robustness, both methods lead to similar results.…”
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Distributed Robust Low-Carbon Economic Dispatch of Power Systems Considering Extreme Scenarios by HU Heng, QIN Jianru, LI Haibo, LIU Jiefeng

“…[Methods] To address this， this paper first constructs RE generation scenarios using Latin hypercube sampling （LHS） and modified k-means clustering， verifying their reserve feasibility， while transforming reserve-infeasible scenarios into extreme scenario sets. …”
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Incorporating Risk in Operational Water Resources Management: Probabilistic Forecasting, Scenario Generation, and Optimal Control by Ties van derHeijden, Miguel Angel Mendoza‐Lugo, Peter Palensky, Nick van deGiesen, Edo Abraham

“…Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. …”
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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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Variational Quantum Monte Carlo Solution of the Many-Electron Schrödinger Equation Based on Deep Neural Networks by Huiping Su, Hongbo Gao, Xinmiao Wang, Xi He, Da Shen

“…Therefore, it is crucial to find an effective numerical method. To solve this problem, this paper present a deep learning architecture, VMCNet, using the powerful computational efficiency of neural networks to improve the speed of numerical computation. …”
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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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Determinate arbitrary quantum state engineering through one-dimensional quantum walks by Yu Wang, Meng Li, Yuan-Yuan Zhao

“…This efficient method for preparing quantum superposition states lays a foundation for advancing quantum technologies, with broad applications in quantum information processing.…”
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Multiscale Sieve for Smart Prime Generation and Application in Info-Security, IoT and Blockchain by Gerardo Iovane, Elmo Benedetto, Carmine Gallo

“…The huge computational cost required to test whether a number is prime and the inefficiency of the known sieving algorithms for extremely large inputs have posed significant challenges in computational number theory. Traditional deterministic prime generation methods struggle to maintain performance when the input sizes increase exponentially. …”
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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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Digital twin and contact analysis of ultra-long distance coiled tubing operation structures [version 2; peer review: 1 approved, 2 approved with reservations] by Wenlan Wei, Jiarui Cheng, Hao Qu, Maliang Wang, Wenyuan Wang

“…Background Coiled tubing operation is a high-quality operation method that considers both cost and efficiency in current oil and gas well operations. …”
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