Optimising 3D-printed carbon fibre composites using machine learning: Balancing strength and efficiency by José Humberto S. Almeida, Jr., Guilherme Ferreira Gomes

“…This study introduces a data-driven framework that integrates machine learning (ML) and genetic algorithms (GA) to optimise interlaminar strength and minimise printing time simultaneously. …”
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Different strokes: differences in the characteristics and outcomes of BCVI and non-BCVI strokes in trauma patients by Lillian S Kao, Charles E Wade, Xu Zhang, Bryan A Cotton, Sean I Savitz, Michelle K McNutt, Cedar Slovacek, David Rosenbaum, Hari Kishan Reddy Indupuru, John Harvin

“…Blunt cerebrovascular injury (BCVI)–related strokes and mortality have decreased, likely due to refined screening and treatment algorithms in trauma literature; however, there is a paucity of research addressing non-BCVI strokes in trauma. …”
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Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems by Mi Gan, Xinyuan Li, Fadong Zhang, Zhenggang He

“…We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms.…”
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Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. …”
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Disjunctive and conjunctive decompositions of incompletely defined Boolean functions in a Binary Decision Diagram by P. N. Bibilo

“…The application of the proposed algorithms and already known algorithms for minimizing multilevel BDD representations of partial function systems allows us to obtain the best results of technologically independent logical optimization, the initial stage of logic circuit synthesis.…”
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Artificial Intelligence‐Based Approaches for AAV Vector Engineering by Fangzhi Tan, Yue Dong, Jieyu Qi, Wenwu Yu, Renjie Chai

“…The advancement of artificial intelligence (AI), particularly machine learning, offers significant potential to accelerate capsid optimization while reducing development time and manufacturing costs. …”
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Advancing Stability in Robot Manipulators: A Review of Recent Progress and Parameters by Shabnom Mustary, Mohammod Abul Kashem, Jannatul Mawya Sony, Nayem Hossain, Mohammad Asaduzzaman Chowdhury

“…ABSTRACT In this review, we present an overview of the current state‐of‐the‐art in robotics while emphasizing stability and control aspects with pragmatic consideration over robotic manipulators. …”
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Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation by Nizam Sohail, Codi Allison, Rogawski McQuade Elizabeth, Benkeser David

“…However, few of these methods are interpretable, while those that do emphasize interpretability often suffer in terms of performance. …”
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Design and Implementation of an Ultra-Low-Power Hazardous Gas Monitoring System by Hongyu Liu, Yuchen Wang, Jiankang Yu, Shuqing Wang, Huijuan Chen

“…In addition, the system optimizes data acquisition and processing algorithms to segment gases of different concentrations. …”
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Scheduling Approach for the Simulation of a Sustainable Resource Supply Chain by Henning Strubelt, Sebastian Trojahn, Sebastian Lang, Abdulrahman Nahhas

“…The process sequence is determined by means of a priority index. Genetic algorithms are then applied to improve the priority index to further increase performance. …”
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Method for designing specialized computing systems on the basis of hardware and software cooptimization by I. E. Tarasov, P. N. Sovietov, D. V. Lulyava, N. A. Duksin

“…This is due to the specifics of the algorithms for placing and routing components of digital devices, which are not capable of optimizing solutions in a strict mathematical sense in an acceptable time. …”
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Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches by Cvetkovski Goga, Petkovska Lidija

“…In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. …”
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Artificial intelligence in educational technology and transformative approaches to English language using fuzzy framework with CRITIC-TOPSIS method by Jingdan Liu, Xujie Bao, Liji Chen

“…Abstract Artificial intelligence (AI) is transforming educational technology by enabling personalized, adaptive, and data-driven learning experiences. Machine learning algorithms analyze student performance to tailor content delivery, while natural language processing facilitates interactive learning through voice assistants and essay evaluation. …”
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Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems by Benedetta Fasciolo, Nicolò Grasso, Giulia Bruno, Paolo Chiabert

“…Abstract As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. …”
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Smart extrusion via data-driven prediction of grain size and peripheral coarse grain defect formation by Marco Negozio, Lorenzo Donati, Adrian H. A. Lutey

“…Abstract Optimization of the mechanical and crash properties of extruded AA6XXX automotive profiles requires detailed understanding of grain size evolution and formation of the peripheral coarse grain (PCG) defect. …”
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AHA: Design and Evaluation of Compute-Intensive Hardware Accelerators for AMD-Xilinx Zynq SoCs Using HLS IP Flow by David Berrazueta-Mena, Byron Navas

“…Our performance evaluation across various configurations highlights performance–resource trade-offs and demonstrates that ANN and BPNN achieve significant parallelism, while AES optimization increases resource utilization the most. …”
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Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

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The role of precision tool positioning in enhancing machining accuracy by Tuyboyov Oybek, Baydullayev Azamat, Jeltuxin Andrey, Muxiddinov Zayniddin

“…Hybrid optimization techniques, including genetic algorithms, effectively balanced the trade-off between precision and productivity, achieving a 10% reduction in production time without sacrificing accuracy. …”
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Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning by Ming Sun, Yihe Zhong, Xiaoou He, Jie Zhang

“…To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. …”
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