Optimizing Energy Consumption and Latency in IoT Through Edge Computing in Air–Ground Integrated Network With Deep Reinforcement Learning by Vitou That, Kimchheang Chhea, Jung-Ryun Lee

“…Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.…”
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Highly durable and energy‐efficient probabilistic bits based on h‐BN/SnS2 interface for integer factorization by Joon‐Kyu Han, Jun‐Young Park, Shania Rehman, Muhammad Farooq Khan, Moon‐Seok Kim, Sungho Kim

“…Recent advancements in probabilistic computing approaches have demonstrated significant potential for addressing these problems more efficiently than conventional deterministic computing methods. …”
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Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals by Zhang Qian, Mingyue Guo, Fuwang Wang

“…By comparing the EEG signals of workers with and without reward and punishment incentives (control group vs. experimental group), key features such as deterministic (DET) and average diagonal line length (DLL) are extracted to reveal how incentives regulate work efficiency. …”
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Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control by Farhat Mahmood, Rajesh Govindan, Tareq Al-Ansari

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A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track by Jingchen Pu, Mu Mu, Jie Feng, Xiaohui Zhong, Hao Li

“…Although emerging artificial intelligence (AI)-based weather models offer high forecast accuracy and improved computational efficiency, they still face considerable challenges in ensemble forecasting applications, due to the unclear error growth dynamic and the lack of suitable ensemble methods in AI-based models. …”
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Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization by Alif Rahmatullah Umar, Hasan Albinsaid, Chia-Po Wei, Chih-Peng Li

“…We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). …”
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Efficient IP address retrieval using a novel octet based encoding technique for high speed lookup to improve network performance by Veeramani Sonai, Indira Bharathi, Sajjad Shaukat Jamal, Zaid Bassfar, Sameer Abdullah Nooh

“…This paper proposes an efficient deterministic approach to encoding IP prefixes that reduces storage complexity. …”
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Identification and Valuation of Economic Activities Reserves as a Priorityin Strategic Analysis by Svetlana Evgen’evna Egorova, Natal’ya Nikolaevna Kistaeva

“…Comparative characteristics of the major methods of reserves valuation: method of direct evaluation, method of comparison, deterministic factor analysis, correlation and regression analysis, value analysis, mathematical programming are provided. …”
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Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning by Charilaos Latinopoulos, Efstathios Zavvos, Dimitrios Kaklis, Veerle Leemen, Aristides Halatsis

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Reinforcement learning for autonomous underwater vehicles (AUVs): navigating challenges in dynamic and energy-constrained environments by Mohab M. Eweda, Karim ElNaggar

“…Reinforcement Learning (RL) has arisen as a revolutionary method for tackling these challenges. This paper examines significant progress in reinforcement learning algorithms, emphasizing their application in the training of autonomous underwater vehicles in both simulated and real-world environments. …”
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Integrated estimation of parameters of radio transmitter power amplifier with automatic mode adjustment by two-frequency test signal by P. V. Sak

“…Method is proposed for calculation of energy gain and efficiency factor (efficiency) when applying automatic control of supply voltage of output cascades of shortwave transmitters intended for modulation with speech signals. …”
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MM3: Multimodal framework for regional-scale quantitative landslide risk analysis by William Pollock, Joseph Wartman

“…However, to date, no readily transferable, regional-scale method for PLRA exists. In this work, we expand an existing deterministic multimodal method for landslide risk analysis developed in the country of Lebanon into a linked framework of code-based modules that are location-agnostic and computationally efficient for regional end-to-end risk estimation. • Use of near-global, remote-sensing-based inputs enables risk estimates almost anywhere in the world • Modular computational framework facilitates upgrades of component models as new research becomes available • Probabilistic implementation through a Monte Carlo approach…”
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A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model by Edwiga Renald, Miracle Amadi, Heikki Haario, Joram Buza, Jean M. Tchuenche, Verdiana G. Masanja

“…This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. …”
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Maximum a posteriori estimation for high-throughput peak fitting in X-ray photoelectron spectroscopy by Tarojiro Matsumura, Naoka Nagamura, Shotaro Akaho, Kenji Nagata, Yasunobu Ando

“…The computation is performed efficiently by the spectrum-adapted expectation–conditional maximisation algorithm with deterministic annealing. …”
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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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RELIABILITY-BASED DESIGN OPTIMIZATION USING OPTIMUM SAFETY FACTORS FOR LARGE-SCALE PROBLEMS by Gh. Kharmanda, I. R. Antypas

“…A numerical application on a large scale problem under  fatigue  loading  shows  the  efficiency of the developed RBDO method relative to the Deterministic Design Optimization (DDO). …”
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A two-stage computational approach for stochastic Darcy-forchheimer non-newtonian flows by Muhammad Shoaib Arif, Muhammad Shoaib Arif, Kamaleldin Abodayeh, Yasir Nawaz

“…The proposed two-stage numerical method integrates a modified time integrator with a second-stage Runge-Kutta scheme, ensuring second-order accuracy in time for deterministic problems. …”
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Rapid Probabilistic Inundation Mapping Using Local Thresholds and Sentinel-1 SAR Data on Google Earth Engine by Jiayong Liang, Desheng Liu, Lihan Feng, Kangning Huang

“…Traditional inundation mapping often relies on deterministic methods that offer only binary outcomes (inundated or not) based on satellite imagery analysis. …”
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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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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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