Applicability Evaluation of Provincial Precipitation Real-time Analysis Product in Beijing by Zhao Wenfang, Wang Huiying, Meng Huifang, Miao Yupeng, Huang Mingming, Fan Min, Tang Wei

“…The national precipitation real-time analysis product is a gridded product developed using probability density matching, Bayesian model averaging, multi grid variation, optimal interpolation and other technologies by National Meteorological Information Center. …”
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Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study by Shi Yu Li, Yue Ming Li, Yong Qi Fang, Zhi Ying Jin, Jun Kang Li, Xiao Meng Zou, Si Si Huang, Rui Lan Niu, Nai Qing Fu, Yu Hong Shao, Xuan Tong Gong, Mao Ran Li, Wei Wang, Zhi Li Wang

“…LASSO regression was employed to select radiomic features (RFs), while eight machine learning algorithms were utilized to construct radiomic models based on US, CDFI, and CEUS. …”
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Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach by Mohammed Aly, Mohamed H. Behiry

“…Abstract Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. …”
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SLR Systematic Literature Review: Metode Penilaian Kinerja Karyawan Menggunakan Human Performance Technology by Roni Habibi, Artha Glory Romey Manurung

“…Therefore, this systematic literature review is carried out to identify the methods used for employee performance appraisal and mentions that optimization algorithms and fuzzy comprehensive methods are used as evaluation methods to assess performance appraisal methods. …”
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Enhancing Automated Maneuvering Decisions in UCAV Air Combat Games Using Homotopy-Based Reinforcement Learning by Yiwen Zhu, Yuan Zheng, Wenya Wei, Zhou Fang

“…While algorithms enhanced with densely distributed artificial rewards show potential, they risk deviating from the primary objectives. …”
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An efficient approach towards index structures for skyline queries by Huaiguang Song, Zhongbo Wu, Binglei Guo, Zhao Wu, Min Wang

“…Extensive experiments show that U-INDG outperforms the most efficient index-based skyline algorithms currently available. It significantly reduces query time across various types of datasets, with speedups ranging from approximately 2.4 $$\times $$ × to 7.8 $$\times $$ × compared to state-of-the-art algorithms, while ensuring stable performance.…”
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Two Methods for Superposing the Structures of Like-Molecule Assemblies: Application to Peptide and Protein Oligomers and Aggregates by Adam Liwo, Mateusz Leśniewski

“…Two algorithms are proposed for the superposition of assemblies of like molecules (e.g., peptide and proteins homooligomers and homoaggregates), which do not require examining all permutations of the molecules. …”
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Safety of lifitegrast: A real-world pharmacovigilance study based on FAERS. by Chu Zhang, Hui Wang, Xianglei Chen, Yong Liu, Puen Jiang

“…AEs not mentioned in the drug labeling were identified, such as glaucoma and cataracts.AEs associated with Lifitegrast are more commonly observed in male patients and those aged over 65 years.The insights derived from this FAERS database analysis are significant for optimizing the use of Lifitegrast while minimizing potential side effects, thereby enhancing the safety of its clinical application.…”
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A dynamic cobweb resistance network solution based on a structured zeroing neural network and its applications by Xiaoyu Jiang, Dexin Meng, Yanpeng Zheng, Zhaolin Jiang

“…By leveraging the hidden structural attributes of a Laplacian matrix, the study devises optimized algorithms for the neural network models, which markedly improve computational efficiency. …”
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Load balancing for high performance computing using quantum annealing by Omer Rathore, Alastair Basden, Nicholas Chancellor, Halim Kusumaatmaja

“…However, for the more complex particle formulation, approached as a multiobjective optimization, quantum annealing solutions are demonstrably Pareto dominant to state of the art classical methods across both objectives. …”
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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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Advances and challenges in neoantigen prediction for cancer immunotherapy by Yi Zhang, Ting-Ting Chen, Xiong Li, Ai-Lin Lan, Peng-Fei Ji, Ya-Juan Zhu, Xue-Yao Ma

“…Future advancements will focus on dynamic tumor microenvironment monitoring, multi-omics integration, improved computational models and algorithms to refine neoantigen prediction, and developing optimized personalized vaccines.…”
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Multi-Agent Reinforcement Learning in Games: Research and Applications by Haiyang Li, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, Donglin Zhu

“…The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems.…”
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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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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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Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making by Oreofeoluwa A. Akintan, Kifle G. Gebremedhin, Daniel Dooyum Uyeh

“…This review connects data-driven decision-making in optimizing feed formulation to enhance milk quantity and quality while addressing animal health implications. …”
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A new temporal locality-based workload prediction approach for SaaS services in a cloud environment by Wiem Matoussi, Tarek Hamrouni

“…The method will be implemented in order to simultaneously achieve a twofold benefit: obtain precise forecast results while optimizing response time. In this regard, we have chosen to control the computation time by dynamizing the size of the sliding window associated to the recent history to be analyzed, since the larger the size of the entry in the prediction model, the more the algorithmic complexity increases. …”
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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…The proposed procedure ensures building models of outstanding predictive power while using small training datasets, which is beyond the capabilities of benchmark algorithms.…”
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