A study on multi-objective optimization for the location selection of smart underground parking facilities in high-density urban areas of megacities: A case study of Jing'an distri... by Xiaodan Li, Yunci Guo, Zhen Liu, Dandan Sun, Yidi Liu, Wencan Wang

“…The model is solved using an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), which dynamically adjusts crossover and mutation rates. …”
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EDT-MCFEF: a multi-channel feature fusion model for emergency department triage of medical texts by Tao Lin, Shiming Yi

“…The model employs a hybrid masking approach and RoBERTa (Robustly Optimized BERT Approach) to facilitate feature enhancement and word vector processing of text. …”
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Time-triggered stream scheduling method combining no-wait and time-slot mapping reuse by HE Qian, GUO Yanan, ZHAO Baokang, PAN Qi, WANG Yong

“…Finally, several time-triggered stream scheduling optimization functions were given and solved based on an improved multi-objective genetic algorithm. …”
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Application of data twinning based on deep time series model in smart city traffic flow prediction by Li Gao

“…The information fusion mechanism combines real-time data and forecast data to optimize the model inputs, and the PSO algorithm is used to optimize the model parameters. …”
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Research on Location Planning of Battery Swap Stations for Operating Electric Vehicles by Pengcheng Ma, Shuai Zhang, Bin Zhou, Wenqi Shao, Haowen Li, Tengfei Ma, Dong Guo

“…The results show that the MLP-NSGA-II algorithm improves the convergence efficiency by about 30.23%, and the service coverage of the optimal solution reaches 94.30%; the service utilization rate is 85.35%, which is 6.25% and 19.69% higher than that of the GA and DPC, respectively. …”
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Multi-Agent Coordinated Dispatch of Power Grid and Pumped Hydro Storage with Embedded Market Game Model by LOU Wei, HU Rong, YU Jinming, ZHANG Xipeng, FAN Feilong, LIU Songyuan

“…To formulate the proposed scheduling strategy, a bi-level optimization problem with an embedded game model is solved: the decision-making problem of the pumped storage power station participating in the electric energy spot market, and the optimization with the embedded marketing game model of capacity allocation and power scheduling strategy about pumped storage. …”
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Improving Vehicle Dynamics: A Fractional-Order PI<i>^λ</i>D<i>^μ</i> Control Approach to Active Suspension Systems by Zongjun Yin, Chenyang Cui, Ru Wang, Rong Su, Xuegang Ma

“…A fractional-order PI<i>^λ</i>D<i>^μ</i> (FOPID) controller was proposed, and its structural parameters were optimized using a gray wolf optimization algorithm. …”
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Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks by Shahzar Hafeez, Ayesha Ishaq, Azeem Intisar, Tariq Mahmood, Muhammad Imran Din, Ejaz Ahmed, Muhammad Rizwan Tariq, Muhammad Amin Abid

“…To overcome these limitations, the modeling and optimization of water treatment methods is required. …”
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Research on Maneuvering Motion Prediction for Intelligent Ships Based on LSTM-Multi-Head Attention Model by Dongyu Liu, Xiaopeng Gao, Cong Huo, Wentao Su

“…The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance.…”
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Extended State Observer-Based Robust Model Predictive Velocity Control for Permanent Magnet Synchronous Motor by Eunji Lee, Yonghao Gui, Sesun You, Jun Moon, Wonhee Kim

“…Thus, the proposed method is robust against external disturbances and parameter uncertainties owing to feedback linearization, state feedback, and ESO-based MPC using the acceleration PMSM model. The proposed control algorithm was experimentally verified and it showed improved velocity tracking performance compared with ESO-based MPC using the conventional PMSM model.…”
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Prediction of Urban Rail Transit Train Door Faults Based on FOA-BP Neural Network Model by WEN Kaiyue, QIU Weibin, DING Xianze, OU Hongxiang

“…[Method] Taking the abnormal current signal of URT train door before failure as the research object, a FIR (finite impulse response) filter is designed to filter and dimensionally normalize the collected URT train door current signal data; the FOA (fruit fly optimization algorithm)-BP (back propagation) neural network model is used to train the closed state learning sample data of different doors after dimensional normalization, and output test results; FOA-BP results and output results after BP neural network models training are compared and analyzed. …”
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Dynamic Multibody Modeling of Spherical Roller Bearings with Localized Defects for Large-Scale Rotating Machinery by Luca Giraudo, Luigi Gianpio Di Maggio, Lorenzo Giorio, Cristiana Delprete

“…In conclusion, the developed model represents a promising tool for generating useful datasets for training diagnostic and prognostic algorithms, thereby contributing to the improvement of predictive maintenance strategies in industrial settings. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling by Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet

“…This study introduces an open-loop control system designed to improve the accuracy of extrusion-based bioprinting techniques, which is composed of a specific experimental setup and a series of algorithms and models. …”
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Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System by Boyu Shu, Zhiwu Huang, Wanwan Ren, Yue Wu, Heng Li

“…Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. …”
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Developing an Interpretable Machine Learning Model for Early Prediction of Cardiovascular Involvement in Systemic Lupus Erythematosus by Deng Z, Liu H, Chen F, Liu Q, Wang X, Wang C, Lyu C, Li J, Li T

“…Notably, the GBM model showed optimal performance, and its interpretability allowed clinicians to visualize decision-making processes, facilitating early identification of high-risk patients.Keywords: systemic lupus erythematosus, cardiovascular involvement, machine learning, prediction model, interpretability…”
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Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning by Shupei Zhang, Huapeng Shi, Wei Zhang, Ying Pang, Pengju Sun

“…Moreover, traditional optimization algorithms make it difficult to achieve timeliness in real-time changing traffic conditions. …”
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Intelligent decision-making and regulation method of gas extraction “borehole-pipe network” system by Kai WANG, Dongxu WANG, Aitao ZHOU, Junwen ZHANG, Fangzhou SONG, Chang’ang DU, Yushuang HAO, Xihui FAN, Wei ZHAO

“…Based on the improved particle swarm optimization algorithm, the intelligent optimization decision-making and regulation model of pipeline network is constructed. …”
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Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning by Son V.T. Dao, Tuan M. Le, Hieu M. Tran, Hung V. Pham, Minh T. Vu, Tuan Chu

“…Moreover, in the proposed framework, the data augmentation approach has been utilized to improve the model’s performance by increasing the amount of samples. …”
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ARMC-RL: Adaptive Caching With Reinforcement Learning for Efficient 360&#x00B0; Video Streaming in Edge Networks by Minji Choi, Somin Park, Jin-Hyun Ahn, Dong Ho Kim, Cheolwoo You

“…In particular, ARMC improved the hit rate by up to 29% compared to Least Frequency Used algorithm under constrained cache conditions of 6% or less of the total data. …”
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