Integral Reinforcement Learning-Based Online Adaptive Dynamic Event-Triggered Control Design in Mixed Zero-Sum Games for Unknown Nonlinear Systems by Yuling Liang, Zhi Shao, Hanguang Su, Lei Liu, Xiao Mao

“…In this paper, multiplayer mixed zero-sum games (MZSGs) are studied by the means of an integral reinforcement learning (IRL) algorithm under the dynamic event-triggered control (DETC) mechanism for completely unknown nonlinear systems. …”
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Solar PV focused LFC studies utilizing an SFS-optimized PID with fractional derivative (PIDDμ), and incorporating BESS and FESS applications by S.B. Masikana, Gulshan Sharma, Sachin Sharma, Pitshou N. Bokoro, Emre Çelik

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Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.) by Fazilat Fakhrzad, Warqaa Muhammed ShariffAl-Sheikh, Mohammed M. Mohammed, Heidar Meftahizadeh

“…To identify optimal combinations of genotype and planting date for maximizing morphological traits, the RF model was integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). …”
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Prediction of Vehicle Interior Wind Noise Based on Shape Features Using the WOA-Xception Model by Yan Ma, Hongwei Yi, Long Ma, Yuwei Deng, Jifeng Wang, Yudong Wu, Yuming Peng

“…The key hyperparameters of the Xception model are adaptively optimized using the whale optimization algorithm to improve the prediction accuracy and generalization ability of the model. …”
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A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF by Zenzen, Roumaissa, Ayadi, Ayoub, Benaissa, Brahim, Belaidi, Idir, Sukic, Enes, Khatir, Tawfiq

“…Comparative evaluations with conventional optimisation algorithms, namely Cuckoo, Bat, and Particle Swarm Optimisation, reveal similar Mean Percentage Error values but with increased result variability, whereas Deep Artificial Neural Network models with varied hidden layer sizes.…”
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Research on the Range of Stiffness Variation in a 2D Biomimetic Spinal Structure Based on Tensegrity Structures by Xiaobo Zhang, Zhongcai Pei, Zhiyong Tang

“…This paper presents a novel variable stiffness mechanism, namely the SBTDTS (Spinal Biomimetic Two-Dimensional Tensegrity Structure), which is constructed by integrating bioinspiration derived from biological spinal structures with the T-Bar mechanical design within tensegrity structures. …”
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Multiscale Feature Modeling and Interpretability Analysis of the SHAP Method for Predicting the Lifespan of Landslide Dams by Zhengze Huang, Yuqi Bai, Hengyu Liu, Yun Lin

“…This study proposes a hybrid CNN–Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. …”
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Relaxation Parameter Optimization in Electrical-to-Mechanical Co-Simulation Based on Time Windowing WR Technique by Md Moktarul Alam, Richard Perdriau, Mohammed Ramdani, Mohsen Koohestani

“…Additionally, a genetic algorithm (GA) is applied to refine relaxation parameters (e.g., impedance values), effectively addressing challenges posed by nonlinear device behaviors and improving the accuracy of co-simulation results. …”
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High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management by Zihao Wang, Qi Gao, Michele Crosetto, Maria Jose Escorihuela

“…Fine-scale accurate SM products hold more and more significant value to water management and precise agricultural irrigation. …”
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Calculation Model of Multi-roll Straightening Process Based on Bilinear Hardening and Power Hardening by ZHU Xiaoyu, CHENG Zixing, WANG Xiaogang, HAN Peisheng

“…A least squares algorithm was employed to solve the nonlinear equations iteratively, enabling accurate straightening calculations. …”
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Synthesis of evasive maneuver control of unmanned aerial vehicle for terminal restrictions by N. Y. Polovinchuk, S. V. Ivanov, L. I. Kotelnitskaya

“…It is characterized in that instead of the forecast procedure in speed-up time and calculation of residuals, the conversion of a boundary value problem to a one-point task of integration of the ordinary differential equations system with account for the occurrence of non-zero terminal member in the cost functional, is used. …”
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Integrative multi-omics analysis and machine learning reveal the unique role of ASCC3 in combination with various immune-related genes in rectal adenocarcinoma by Chuang Miao, Fei Qian

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Enhanced Multilinear PCA for Efficient Image Analysis and Dimensionality Reduction: Unlocking the Potential of Complex Image Data by Tianyu Sun, Lang He, Xi Fang, Liang Xie

“…EMPCA integrates random singular value decomposition to reduce computational complexity while maintaining data integrity. …”
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Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization by Guozhu Song, Jian Wang, Rongting Ma, Yan Shi, Yaqi Wang

“…Moreover, the fruit stalks being too short or slender poses an obstacle, rendering it impossible for the depth camera to accurately obtain depth information during depth value acquisition. To address these challenges, this paper proposes an enhanced YOLOv8 model integrated with a depth camera for string tomato fruit stalk picking point identification and localization research. …”
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Integration of single-cell and bulk RNA sequencing to identify a distinct tumor stem cells and construct a novel prognostic signature for evaluating prognosis and immunotherapy in... by Fengyun Zhao, Mengting Chen, Tianjiao Wu, Mingfang Ji, Fugui Li

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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. …”
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Improving aboveground biomass estimation by integrating ICESat-2 LiDAR sampling and tree species stratification strategies using multi-source datasets by Pan Liu, Chunying Ren, Zongming Wang, Mingming Jia

“…The AGB estimates exhibited a south-north gradient in all models, while UTSS performed best in both the low and high values. This study demonstrates that the UTSS framework incorporating ICESat-2 LiDAR sampling and tree species stratification strategies provides a promising approach to estimating AGB by integrating freely accessible datasets, algorithms, and platforms.…”
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SbD4Skin by EosCloud: Integrating multi-view molecular representation for predicting skin sensitization, irritation, and acute dermal toxicity by Nikoletta-Maria Koutroumpa, Dimitra-Danai Varsou, Panagiotis D. Kolokathis, Maria Antoniou, Konstantinos D. Papavasileiou, Eleni Papadopoulou, Anastasios G. Papadiamantis, Andreas Tsoumanis, Georgia Melagraki, Milica Velimirovic, Antreas Afantitis

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Optimizing the eco-friendly dyeing of wool and nylon fabrics with Prangos ferulacea (L.) Lindl using artificial intelligence by Shiva Shokrzadeh, Milad Razbin, Fatemeh Shahmoradi Ghaheh, Majid Tehrani, Mehdi Sadrjahani

“…The integration of artificial intelligence techniques provides deeper insights into dyeing mechanisms, advancing optimized, eco-friendly, and efficient textile dyeing methodologies.…”
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Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM by Qingquan Lv, Jialin Zhang, Jianmei Zhang, Zhenzhen Zhang, Qiang Zhou, Pengfei Gao, Haozhe Zhang

“…The results demonstrate that the PSO-CNN-LSTM model achieves lower RMSE, MAE, and MSE values compared to alternative models. Concurrently, its higher R² value indicates superior alignment between model predictions and the dataset. …”
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