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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Building Energy Optimization Using an Improved Exponential Distribution Optimizer Based on Golden Sine Strategy Minimizing Energy Consumption Under Uncertainty by Mohammad Ali Karbasforoushha, Mohammad Khajehzadeh, Suraparb Keawsawasvong, Lapyote Prasittisopin, Thira Jearsiripongkul

“…The results demonstrate that IEDO outperforms other methods, reaching the optimal solution with the lowest annual initial energy consumption of 132.7519 kWh/m²a, compared to 132.9356 kWh/m²a for the grey wolf optimizer (GWO) and 133.0000 kWh/m²a for POSCO, as reported in previous studies for a single office building. …”
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Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG–VSC Algorithm by Zhengyang Cao, Gang Chen

“…However, conventional control methods often struggle with managing continuous action spaces and responding to dynamic environments, making them inadequate for complex multi-UAV cooperative formation control tasks. …”
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Wide-Range Variable Cycle Engine Control Based on Deep Reinforcement Learning by Yaoyao Ding, Fengming Wang, Yuanwei Mu, Hongfei Sun

“…To solve this problem, this paper adopts a deep reinforcement learning method based on a deep deterministic policy gradient algorithm, and it applies an action space pruning technique to optimize the controller, which significantly improves the convergence speed of network training. …”
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Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications by Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai

“…This corroborates that the proposed method can preserve privacy by handling issues related to fairness in model updates and improving efficiency better than state-of-the-art alternatives.…”
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Performance estimation of a steam-turbine driven multistage compressor system by Aleksa Miladinović, Jasna Stajić – Trošić, Mirjana Kijevčanin, Vladimir Stijepović, Sabla Y. Alnouri, Mirko Stijepović

“…As such, the method's effectiveness was demonstrated with model efficiencies closely matching design values within 2–10 % deviation for most stages, and highlighting areas for improvement where deviations reached up to 37 % in later stages.…”
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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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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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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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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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A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays by Na Chen, Meng Wang, Tom Alkim, Bart van Arem

“…A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. …”
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A simple model considering spiking probability during extracellular axon stimulation. by Frank Rattay, Thomas Tanzer

“…However, most computer simulation studies for neuroprosthetic applications calculate thresholds for neural targets with a deterministic model and by reducing the sigmoid curve to a step function, they miss an important information about the control signal, namely how the spiking efficiency increases with stimulus intensity. …”
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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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Joint Resource Allocation for V2X Sensing and Communication Based on MADDPG by Zhiyong Zhong, Zhangyou Peng

“…In this paper, we propose a joint resource allocation method for V2X communication and sensing, aiming to optimize both communication rate and sensing performance. …”
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Improving resilience of networked multi-energy carrier microgrids through proactive scheduling by Ali Akbar Ghasemi, Mohsen Gitizadeh, Mohammadali Norouzi

“…Finally, by performing simulations, the efficiency of the proposed method in improving the resilience of the test NMECMs is evaluated. …”
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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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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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