Design and optical optimization of a 45 kW beam-down solar-thermal concentrator by Ramón Pujol-Nadal, Luis Guerreiro, Gabriel Cardona

“…In this study, we perform an optical analysis of a Cassegrain concentrator using the open-source software OTSunWebApp, a ray-tracing program that allows for the spectral optical analysis of solar collectors. …”
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Optimization of lubricating oil change intervals for marine internal combustion engines: algorithm research and design by Justas Žaglinskis, Laurynas Jagėla

“…This study investigates an algorithm for optimal oil change intervals in the marine engine fleet, which reduces the costs due to the recommendations of ship engine manufacturers, which may have a large margin of error. …”
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Mathematical Modelling and Optimization Methods in Geomechanically Informed Blast Design: A Systematic Literature Review by Fabian Leon, Luis Rojas, Alvaro Peña, Paola Moraga, Pedro Robles, Blanca Gana, Jose García

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Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking by Athanasios Sidiropoulos, Dimitrios Bechtsis, Dimitrios Vlachos

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Estimation and Optimization of the Hydrostatic Height of Waterway Embankment Using Taguchi-Based Honey Badger Algorithm by Swarnima Subhadarsini, Debabrata Giri, Sudhanshu Das

“…The confirmatory test was piloted to check the similarity index between the two methods and the outcomes were found to be nearly similar with an error of 2.62%.…”
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Development and optimization of pearl millet waste biocomposite ceiling tiles: a waste management approach by Khalid Hussain Ansari, Srikanta Routroy, Rahul Samyal, Shivasheesh Kaushik

“…The signal-to-noise (S/N) ratio was used to identify the optimal composition and Analysis of Variance (ANOVA) with regression analysis determines the significance of each factor on thermal conductivity and flexural strength. …”
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Hyperparameter Optimization of Neural Networks Using Grid Search for Predicting HVAC Heating Coil Performance by Yosef Jaber, Pasidu Dharmasena, Adam Nassif, Nabil Nassif

“…Five key hyperparameters were varied systematically: number of epochs, network size, network shape, learning rate, and optimizer. The best-performing model achieved a mean squared error of 0.469 and featured 17 hidden layers, a left-triangle architecture trained for 500 epochs with a learning rate of 5 × 10⁻⁵, and Adam as the optimizer. …”
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Predictive optimization in automotive supply chains: a BiLSTM-Attention and reinforcement learning approach by Asmae Amellal, Issam Amellal, Mohammed Rida Ech-charrat, Hamid Seghiouer

“…This predictive model achieved a Mean Squared Error (MSE) of 0.0525 and an R² value of 0.896, indicating high accuracy and an ability to explain substantial variance in customer behavior. …”
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Optimizing Bundle Block Adjustment for High-Overlap Small-Format Multi-Head Camera Systems by T. Bannakulpiphat, T. Bannakulpiphat, W. Karel, C. Ressl, N. Pfeifer

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Logistics demand prediction using fuzzy support vector regression machine based on Adam optimization by Jing Quan, Yiwen Peng, Liyun Su

“…Precise prediction of logistics demand is essential for optimizing resource allocation efficiency, which is the core focus of this research endeavor. …”
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Simulation of Natural Gas Pipeline Networks Based on Roughness Optimization Algorithm and Global Mesh Refinement by Yi Yang

“…The roughness identification problem is then transformed into an optimization problem by minimizing the error between measured and simulated values. …”
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Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization by Jing-Jing Liu, Jian-Chao Liu

“…The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. …”
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Multi-objective optimization on thermo-structural performance of honeycomb absorbers for concentrated solar power systems by Masoud Behzad, Sébastien Poncet, Cristóbal Sarmiento-Laurel

“…We adopted a multi-objective optimization approach by integrating computational fluid dynamics, heat transfer, and thermal stress analysis. …”
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FUZZY TIME SERIES BASED ON THE HYBRID OF FCM WITH CMBO OPTIMIZATION TECHNIQUE FOR HIGH WATER PREDICTION by Nursyiva Irsalinda, Dera Kurnia Laely, Sugiyarto Surono

“…Subsequently, we assess forecasting accuracy using the Mean Absolute Percent Error (MAPE), where a smaller percentage indicates superior performance. …”
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Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain by Pratixita Bhattacharjee, Piotr Augustyniak

“…In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. …”
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Optimized design of a permanent magnet brushless DC motor for solar water-pumping applications by Aryadip Sen, Bhim Singh, Kumar Mahtani, Arash Moradzadeh, S.M. Muyeen

“…Magnetic and performance characteristics of the optimized motor are analyzed through finite element analysis (FEA). …”
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Intelligent irrigation strategy model for farmland using dung beetle optimization-random forest algorithms by Wenwen Hu, Yong Liu, Jun An, Shipu Xu, Zhiwen Zhou, Mingming An, Xiaokun Guo, Xiang Ma, Wenfei Jiang, Yunsheng Wang

“…This study proposes an optimized machine learning prediction model using the Dung Beetle Optimization-Random Forest (DBO-RF) algorithm, thus improving irrigation predictability. …”
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Air quality prediction using stacked bi- long short-term memory and convolutional neural network in India by S Karkuzhali, Thendral Puyalnithi, R Nirmalan

“…During the training phase, the Adam optimizer is used to fine-tune the model’s hyperparameters, with Mean Squared Error (MSE) serving as the loss function. …”
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Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor by Mateo Alonso, Guillermo Rubinos Rodriguez, Pablo Diez-Valle, Ana Garbayo, Xela Garcia-Santiago, Gonzalo Blazquez Gil

“…Although deploying QAOA on Qmio required specific adaptations to the hardware, we were able to achieve a shift of the probability distribution towards lower-energy solutions without error mitigation techniques, which highlights both QAOA’s potential and the intrinsic challenges of quantum optimization.…”
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Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators by Katia Benamara, Hocine Amimeur, Yanis Hamoudi, Maher G. M. Abdolrasol, Umit Cali, Umit Cali, Taha Selim Ustun

“…A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. …”
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