Improve The Capacity Of Data Transmission In Orbital Angular Momentum Multiplexing By Adjusting Link Structure by Lin Zhao, Ting Jiang, Min Mao, Yangjie Zhang, Hongzhan Liu, Zhongchao Wei, Dongmei Deng, Aiping Luo

“…It can provide a wide range of applications in vortex high-capacity communication.…”
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Real-Time Sensor for Measuring the Surface Temperature of Thermal Protection Structures Based on the Full-Time Domain Temperature Inversion Method by Yuhao Liu, Xiong Zhao, Xiangyu Wei, Pengyu Nan, Fan Zhou, Guoguo Xin, Kok-Sing Lim, Yupeng Zhang, Hangzhou Yang

“…The study investigates the impact of three noise filtering methods on the inversion accuracy, finding that the Savitzky-Golay filtering significantly enhances measurement precision, reducing mean relative error from 18.4% to 6.7%. These results highlight the potential of the proposed real-time sensor method for practical engineering applications, offering a reliable and efficient solution for real-time TPS temperature monitoring.…”
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Landslide Displacement Prediction Model Based on Optimal Decomposition and Deep Attention Mechanism by Shuai Ren, Kamarul Hawari Ghazali, Yuanfa Ji, Samra Urooj Khan

“…Moreover, the Mean Absolute Scaled Error (MASE) analysis confirms the robustness of the model in capturing both short-term fluctuations and long-term trends. …”
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Establishing a generalized model for accurate prediction of higher heating values of substances with large ash fractions by Peng Jiang, Lin Li, Han Lin, Tuo Ji, Liwen Mu, Yuanhui Ji, Xiaohua Lu, Jiahua Zhu

“…Furthermore, the accuracy was compared to previous literature in terms of correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE). Results revealed that this model provided attractive accuracy with R2 = 0.854, RMSE = 0.900, and MAE = 0.773 within a wide range of ash content from 0 to 83.32 wt%. …”
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Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN by Meng Shi, Yiheng Zhao, Weixiang Yu, Yuchong Chen, Nan Chi

“…The experimental results show that up to 1.22 Gb/s over 1.2 m underwater visible light transmission can be achieved by using DBSCAN for PAM7 MISO signals. The measured bit error rate is well under the hard decision-forward error correction threshold of 3.8 × 10^−3.…”
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Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model by Pasupuleti L. Narayana, Saurabh Tiwari, Anoop K. Maurya, Muhammad Ishtiaq, Nokeun Park, Nagireddy Gari Subba Reddy

“…It achieved a mean absolute error (MAE) of 5.633 μm/year for training and 18.86 μm/year for testing, along with a root mean square error (RMSE) of 0.000055, indicating reliable generalization despite the limited dataset size. …”
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Uncertainty Estimation for Photogrammetric Point Clouds of UAV Imagery by D. Huang, D. Huang, D. Huang, R. Qin, R. Qin, R. Qin, R. Qin

“…Nowadays, unmanned aerial vehicles (UAVs) are widely used in various photogrammetric applications to collect high-resolution images for 3D reconstruction. …”
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Finite-Time Current Tracking in Boost Converters by Using a Saturated Super-Twisting Algorithm by Juan-Eduardo Velázquez-Velázquez, Rosalba Galván-Guerra, José-Antonio Ortega-Pérez, Yair Lozano-Hernández, Raúl Villafuerte-Segura

“…The power converters are widely used in several industrial applications where it is necessary to obtain from a fixed voltage another one higher or lower than the original. …”
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Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes by Bo Wu, Xiuli Wang, Bangyan Wang, Yaohong Xie, Shixiong Qi, Wenduo Sun, Qihang Huang, Xiang Ma

“…The residual-based method addresses uncertainties by generating 95% confidence intervals, enhancing the model’s robustness in practical applications. By simulating real-world conditions, this framework provides reliable medium-term forecasts, making it an effective tool for renewable energy system dispatch and precise error control.…”
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Remaining Useful Life Estimation of Used Li-Ion Cells With Deep Learning Algorithms Without First Life Information by I. Sanz-Gorrachategui, Y. Wang, A. Guillen-Asensio, A. Bono-Nuez, B. Martin-del-Brio, P. V. Orlik, P. Pastor-Flores

“…This methodology achieves an average error of only 62 cycles for cells with a lifespan of up to 1200 cycles and a RUL error of less than 10% for deeply aged batteries. …”
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Machine Learning Strategies for Forecasting Mannosylerythritol Lipid Production Through Fermentation: A Proof-of-Concept by Carolina A. Vares, Sofia P. Agostinho, Ana L. N. Fred, Nuno T. Faria, Carlos A. V. Rodrigues

“…An NN provided predictions with a mean squared error (MSE) of 0.69 for day 4 and 1.63 for day 7 and a mean absolute error (MAE) of 0.58 g/L and 1.1 g/L, respectively. …”
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Mesh representation matters: investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models by Robert KOSK, Richard SOUTHERN, Lihua YOU, Shaojun BIAN, Willem KOKKE, Greg MAGUIRE

“…They are used in facial synthesis, compression, reconstruction and animation, avatar creation, virtual try-on, facial recognition systems and medical imaging. These applications require high spatial and perceptual quality of synthesised meshes. …”
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Enhancing the reliability and accuracy of wireless sensor networks using a deep learning and blockchain approach with DV-HOP algorithm for DDoS mitigation and node localization by Bhupinder Kaur, Deepak Prashar, Leo Mrsic, Ahmad Almogren, Ateeq Ur Rehman, Ayman Altameem, Seada Hussen

“…The proposed model presents a strong solution for real-world applications in wireless network environments.…”
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Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air by Jiayu Yang, Huabing Ke, Sunling Gong, Yaqiang Wang, Lei Zhang, Chunhong Zhou, Jingyue Mo, Yan You

“…The performance evaluation results show that for the PM2.5 forecasts, the correlation coefficient (R) is increased by 0.07–0.13, and the mean error (ME) and root mean square error (RMSE) is decreased by 3.2–3.5 and 3.8–4.7 μg/m³. …”
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Low-overhead defect-adaptive surface code with bandage-like super-stabilizers by Zuolin Wei, Tan He, Yangsen Ye, Dachao Wu, Yiming Zhang, Youwei Zhao, Weiping Lin, He-Liang Huang, Xiaobo Zhu, Jian-Wei Pan

“…Abstract To make practical quantum algorithms work, large-scale quantum processors protected by error-correcting codes are required to resist noise and ensure reliable computational outcomes. …”
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Process optimization for improving anti-oxidation performance of silver-coated copper powders by response surface methodology and artificial neural network by Hongbin Yin, Shiwei Fan, Kun Peng, Xiao Li, Zizhen Wang, Yuxin Chen, Ming Zhou

“…However, the anti-oxidation performance of SCCPs directly determines their reliability in practical applications. This study aims to design an efficient approach for optimizing process parameters to enhance anti-oxidation performance of SCCPs. …”
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New energy efficient management approach for wireless sensor networks in target tracking using Vortex Search Algorithm by Shayesteh Tabatabaei

“…In this article, a new method is introduced to optimize energy consumption in wireless sensor networks for mobile target-tracking applications.The proposed method consists of two stages. …”
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Prediction of lithium-ion battery SOC based on IGA-GRU and the fusion of multi-head attention mechanism by Pei Tang, Minnan Jiang, Weikai Xu, Zhengyu Ding, Mao Lv

“…Finally, the prediction performance of the fusion model proposed in this paper is verified by Pycharm simulation, and the average absolute error, root mean square error and maximum prediction error of the model are 1.62%, 1.55% and 0.5%, respectively, which proves that the model can accurately predict the SOC of lithium-ion battery. …”
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Exploring the potential of deep learning models integrating transformer and LSTM in predicting blood glucose levels for T1D patients by Xin Xiong, XinLiang Yang, Yunying Cai, Yuxin Xue, JianFeng He, Heng Su

“…The model's performance is validated using real-world clinical data and error grid analysis. Results On clinical data, the model achieved root mean square error/mean absolute error of 10.157/6.377 (30-min), 10.645/6.417 (60-min), 13.537/7.283 (90-min), and 13.986/6.986 (120-min). …”
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Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting by Andrey K. Gorshenin, Anton L. Vilyaev

“…The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27.7</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the reduction in Mean Absolute Percentage Error (MAPE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared with ML models without probability informing. …”
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