On the accurate computation of expected modularity in probabilistic networks by Xin Shen, Matteo Magnani, Christian Rohner, Fiona Skerman

“…In this paper, we implement and compare our method and various general approaches for expected modularity computation in probabilistic networks. …”
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Modelling of River-Groundwater Interactions under Rainfall Events Based on a Modified Tank Model by Wen Nie, Yong-chang Liang, Lin Chen, Wei Shao

“…The results of the deterministic method of the numerical case and optimized method of the modified tank model matched well.…”
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Leveraging Transfer Learning in Deep Reinforcement Learning for Solving Combinatorial Optimization Problems Under Uncertainty by Fatima Ezzahra Achamrah

“…In recent years, addressing the inherent uncertainties within Combinatorial Optimization Problems (COPs) reveals the limitations of traditional optimization methods. Although these methods are often effective in deterministic settings, they may lack flexibility and adaptability to navigate the uncertain nature of real-world COP/s. …”
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Pareto Optimal Solutions for Stochastic Dynamic Programming Problems via Monte Carlo Simulation by R. T. N. Cardoso, R. H. C. Takahashi, F. R. B. Cruz

“…This new idea is carried out by using Monte Carlo simulations embedded in an approximate algorithm proposed to deterministic dynamic programming problems. The new method is tested in instances of the classical inventory control problem. …”
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Using Hybrid Wavelet-Exponential Smoothing Approach for Streamflow Modeling by Vahid Nourani, Hessam Najafi, Alireza Babaeian Amini, Hitoshi Tanaka

“…The obtained results indicated that combining WT with the ES method and ANN led to more accurate modeling. The proposed methodology (WES2) that used all decomposed subseries separately improved the efficiency of models up to 30% and 10% for the daily dataset and up to 88% and 57% for the monthly dataset, respectively, for the West Nishnabotna and Trinity Rivers.…”
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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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Research on High-Precision Motion Planning of Large Multi-Arm Rock Drilling Robot Based on Multi-Strategy Sampling Rapidly Exploring Random Tree* by Qiaoyu Xu, Yansong Lin

“…This mechanism flexibly applies three sampling methods at different stages of path planning, significantly improving the adaptability and search efficiency of the RRT* algorithm. …”
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System of Interconnected Reactor Models of the Sulfur Recovery Unit with Hydrogen Extraction for Hydrogen Energy in a Fuzzy Environment by Batyr Orazbayev, Kulman Orazbayeva, Kanagat Dyussekeyev, Tursinbay Turymbetov, Ramazan Yessirkessinov, Ainur Zhumadillayeva

“…This study proposes a systematic modeling approach that integrates deterministic, statistical, and fuzzy logic methods to enhance process efficiency and accuracy. …”
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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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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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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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A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays by Na Chen, Meng Wang, Tom Alkim, Bart van Arem

“…Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…The method also achieved a 40.5% improvement in computational efficiency and a 30-50% reduction in system costs, demonstrating its practicality and scalability. …”
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Joint Resource Allocation for V2X Sensing and Communication Based on MADDPG by Zhiyong Zhong, Zhangyou Peng

“…Integrated Sensing and Communication (ISAC) technology is essential for enhancing spectrum efficiency and reducing resource overhead. However, this also demands a more intelligent and efficient resource allocation framework for next-generation vehicular networks. …”
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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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Hybrid Optimization Technique for Solving Economic Dispatch Problem: A Case Study of Nigerian Thermal Power System by Gafari Abiola Adepoju, Muniru Olajide Okelola, Muhammed Adekilekun Tijani

“… Economic Dispatch Problem (EDP) is a power system optimization problem that is required to be solved accurately using an efficient optimization technique. Hybrid optimization solutions have provided better optimum results than either deterministic or non-deterministic optimization methods. …”
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THE CONTROLLER OF FUZZY LOGIC IN THE MANAGEMENT OF TECHNOLOGICAL PROCESSES by S. V. Shilkina, E. N. Fokina

“…However, the classical control methods work well only with a completely deterministic control object and deterministic environment, but for fuzzy information systems and highly complex control object, fuzzy control methods are optimal. …”
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Optimization Design of Trough Solar Power Plant Based on Probabilistic Reliability by Yanping ZHANG, Yuchao ZHANG, Yifei LIU

“…Then, by considering the randomness caused by the uncertainty factor, the uncertainty model of the trough solar power plant based on its solar multiple, the full load hours of storage system and the row spacing distance of the collector was established by neural network-Monte Carlo method. Secondly, the reliability calculation model was established, levelized cost of energy (LCOE), capacity factor (CF) and total generation efficiency were selected as key performance indicators (KPI), then the KPI were optimized based on the reliability indexes of each KPI. …”
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Reliability-Based Topology Optimization Considering Overhang Constraints for Additive Manufacturing Design by Fahri Murat, Irfan Kaymaz, Abdullah Tahir Şensoy

“…This research investigates build direction parameter solutions using deterministic and RBTO algorithms. Topological properties, compliance, sensitivity, and density filters are assessed, alongside optimization techniques like Method of Moving Asymptotes (MMA) criterion and Optimality Criteria (OC). …”
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A Stochastic -Coverage Scheduling Algorithm in Wireless Sensor Networks by Jiguo Yu, Shaohua Ren, Shengli Wan, Dongxiao Yu, Guanghui Wang

“…Coverage is one of the key issues to achieve energy efficiency of a wireless sensor network. Sensor scheduling is one of the most important methods to solve coverage problems. …”
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