Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation by Amirreza Kandiri, Ramin Ghiasi, Maria Nogal, Rui Teixeira

“…Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50 %, leading to a 56 % reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29 % compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.…”
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Exposing Optimal Feature Sets for Enhancing Machine Learning Performance by Hiba Mohammed Al-Marwai, Ghaleb H. Al-Gaphari, Mohammed Mohammed Zayed

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Preparation and performance evaluation of fluorescent nanofluid for oil displacement by Qi Gong, Haitao Li, Xin Ma, Jiating Hao, Fangjun Jia, Hongwen Luo, Sujuan Gao

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Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network by Aditya Pal, Hari Mohan Rai, Saurabh Agarwal, Neha Agarwal

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Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model by ZHANG Yajie, CUI Dongwen

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Study on the Meshing Characteristic of Parallel Axis Line Gear by Chen Yangzhi, Zhu Anshi, Lü Yueling

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Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique by Sarra Adiche, Mhamed Larbi, Djilali Toumi, Riyadh Bouddou, Mohit Bajaj, Nasreddine Bouchikhi, Abdallah Belabbes, Ievgen Zaitsev

“…Fluctuations in voltage and frequency were also maintained at 2% tolerance and 1% tolerance, respectively, across all voltage limits, which is consistent with international norms. Power-sharing errors were reduced by 50% after conducting the robustness tests against the DC supply and load disturbances. …”
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Bias-Reduced Localization for Drone Swarm Based on Sensor Selection by Bo Wu, Bazhong Shen, Yonggan Zhang, Li Yang, Zhiguo Wang

“…Simulation results show that the randomized SDP algorithm proposed in this paper has the optimal localization effect, and moreover, the bias reduction scheme proposed in this paper can make the localization error of the drone swarm reach the Cramér–Rao Lower Bound (CRLB) with a low signal-to-noise ratio (SNR).…”
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Pruned hierarchical Random Forest framework for digital soil mapping: Evaluation using NEON soil properties by Chengcheng Xu, Jingyi Huang, Alfred E. Hartemink, Nathaniel W. Chaney

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A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electrici... by Xinfu Pang, Wen Jia, Haibo Li, Qingzhong Gao, Wei Liu

“…Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. …”
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Constrained search space selection based optimization approach for enhanced reduced order approximation of interconnected power system models by Bala Bhaskar Duddeti, Asim Kumar Naskar, V. P. Meena, Jitendra Bahadur, Ibrahim A. Hameed

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Research on the Time-varying Meshing Stiffness Algorithm of Spur Gears with Large Tooth Number Cracks by Jiao Wei, Xu Kejun, Qin Haiqin

“…According to the degree of cracks, the modeling suggestion for the effective tooth thickness reduction limit line is given: when the crack level is low, the effective tooth thickness reduction limit line should be a straight line; when the crack level is high, using a straight line will cause significant errors. …”
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A Method for Automatic Feature Points Extraction of Pelvic Surface Based on PointMLP_RegNet by Wei Kou, Rui Zhou, Hongmiao Zhang, Jianwen Cheng, Chi Zhu, Shaolong Kuang, Lihai Zhang, Lining Sun

“…ABSTRACT The success of robot‐assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature‐based registration. …”
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Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…The High Peak-to-Average Power Ratio (PAPR) remains a critical challenge in Non-Orthogonal Multiple Access (NOMA) waveforms, particularly with growing subcarrier configurations. Traditional PAPR reduction methods, including clipping, filtering, and pre-coding, tend to compromise the bit error rate (BER), add spectral inefficiency, or incur high computational complexity, rendering them unsuitable for real-time use in 5G and future wireless systems. …”
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Neural ODE-Based Dynamic Modeling and Predictive Control for Power Regulation in Distribution Networks by Libin Wen, Jinji Xi, Hong Hu, Li Xiong, Guangling Lu, Tannan Xiao

“…The NODE model demonstrates high accuracy in predicting the dynamic behavior in a DN against a detailed simulator, with maximum relative errors below 0.35% for active power. The linearized NODE-MPC controller shows effective tracking performance, constraint handling, and computational efficiency, with typical QP solve times below 0.1 s within a 0.1 s control interval. …”
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PCA-FSA-MLR Model and Its Application in Runoff Forecast by GUO Cunwen, CUI Dongwen

“…To improve the accuracy of runoff forecast,and establish a runoff forecast model combining principal component analysis (PCA),future search algorithm (FSA),and multiple linear regression (MLR),this paper reduces the dimensionality of the sample data by PCA,selects 8 standard test functions and simulates and verifies FSA under different dimensional conditions,optimizes MLR constant terms and partial regression coefficients by FSA,proposes a PCA-FSA-MLR runoff forecast model,constructs PCA-LS-MLR,PCA-FSA-SVM,and PCA-SVM models with dimensionality reduction processing by PCA and FSA-MLR,LS-MLR,FSA-SVM,and SVM without dimensionality reduction processing as a comparison model,and verifies each model through forecasting the annual runoff and monthly runoff in December of Longtan station in Yunnan Province.The results show that:①FSA has better optimization accuracy and global extremum search ability under different dimensional conditions;②The average absolute relative error of the annual runoff and monthly runoff in December of Longtan station through PCA-FSA-MLR model are 1.63% and 3.91% respectively,and its forecast accuracy is better than the other 7 models,with higher forecast accuracy and stronger generalization ability;③For the same model,the forecast accuracy after dimensionality reduction processing by PCA is better than that without dimensionality reduction processing,so the data dimensionality reduction by PCA is helpful to improve the forecast accuracy of models.…”
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Revised Lie Group Analysis of the Time Fractional (2+1)-Dimensional Zakharov-Kuznetsov (<i>q</i>, <i>p</i>, <i>r</i>) Equation by Jian-Gen Liu, Yu-Feng Zhang, Jing-Qun Wang

“…Our findings not only correct previous errors in the literature but also introduce new results, such as the Lie transformation group and optimal system for this model. …”
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Non-intrusive Load Decomposition Model Based on Deep Fusion of Multi-modal Integration by YAO Gang, WANG Yun, WANG Yuanliang, SONG Zihao

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Asymmetrically Reconstructed Optical OFDM for Visible Light Communications by Jindan Xu, Wei Xu, Hua Zhang, Xiaohu You

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Fabry Disease: An Update Based on Evidence and Experience by J. Politei, F. Perretta, H. Trimarchi, N. Antongiovanni, G. Cabrera, A. Fainboim, A. Fernández, F. Gómez Pizarro, M. Pereyra, S. Jaurretche

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