Scaling Laws for Emulation of Stellar Spectra by Tomasz Różański, Yuan-Sen Ting

“…Specifically, given a tenfold increase in training compute, achieving an optimal seven-fold reduction in mean squared error necessitates an approximately 2.5-fold increase in dataset size and a 3.8-fold increase in model size. …”
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Development of regional mixed-effects height–diameter models for natural black pine stands by Ramazan Ozçelik, Onur Alkan

“…Compared to the fixed-effects model, the mixed-effects model achieved a 32% reduction in the root mean square error (RMSE). The findings suggest that the proposed model is highly suitable for forest inventory studies to predict tree heights in black pine stands.…”
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Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

“…Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction.…”
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Improvement of methodical tools of the seasonal transportation irregularity assessment by D. A. Macheret, A. Yu. Ledney

“…This issue negatively affects operation of the railway transport as higher irregularity of transportation means limitation of the overall volume that can be realized within a year, which results in reduction of effectiveness of the industry resources use.When assessing the seasonal irregularity of transportation by means of the method, significant error takes place due to different number of days in the months. …”
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Multi‐Model Ensembles for Upper Atmosphere Models by S. Elvidge, S. R. Granados, M. J. Angling, M. K. Brown, D. R. Themens, A. G. Wood

“…A non‐negative least squares weighting for the MME on a set of bias corrected models provides a 68% and 50% reduction in the mean square error compared to the best model (Jacchia‐Bowman 2008) in the solar minimum and maximum cases, respectively.…”
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AHerfReLU: A Novel Adaptive Activation Function Enhancing Deep Neural Network Performance by Abaid Ullah, Muhammad Imran, Muhammad Abdul Basit, Madeeha Tahir, Jihad Younis

“…We propose AHerfReLU, a novel activation function that combines the rectified linear unit (ReLU) function with the error function (erf), complemented by a regularization term 1/1+x2, ensuring smooth gradients even for negative inputs. …”
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Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems by Ali Gashtasbi, Mário Marques da Silva, Rui Dinis

“…Collectively, these strategies lead to a significant reduction in the Bit Error Rate (BER) and a remarkable improvement in the overall system performance, offering a practical solution for complex LIS deployments.…”
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Market Phases and Price Discovery in NFTs: A Deep Learning Approach to Digital Asset Valuation by Ho-Jun Kang, Sang-Gun Lee

“…Analyzing 26,287 h of transaction data across major marketplaces, the model demonstrates superior predictive accuracy compared to conventional approaches, achieving a 33.5% reduction in Mean Absolute Error versus LSTM models, a 29.7% improvement over Transformer architectures, and a 30.1% enhancement compared to LightGBM implementations. …”
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Adaptive Backstepping Fuzzy Control Based on Type-2 Fuzzy System by Ll Yi-Min, Yue Yang, Li Li

“…This approach adopts type-2 fuzzy system instead of type-1 fuzzy system to approximate the unknown functions. With type-reduction, the type-2 fuzzy system is replaced by the average of two type-1 fuzzy systems. …”
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Investigation of contact resistivity for Au–Ti–Pd–n-Si ohmic contacts for impatt diodes by V. V. Basanets, V. S. Slepokurov, V. V. Shinkarenko, R. Ya. Kudrik, Ya. Ya. Kudrik

“…A method is proposed for reduction of error in determination of contact resistivity based on analysis of statistical dependences of the measured contact resistivity values (which are in the range of (0.9 – 2.0).10–5 Ω.cm2). …”
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Implementation of a Low- Cost Multi- IMU by Using Information Form of a Steady State Kalman Filter by A. M. Shahri, R. Rasoulzadeh

“…Results of static test for z-axis show that ARW coefficient reduces to 0.0022°/√s and VRW error isdecreased by %50. Also, dynamic test results show the reduction of the standard deviation of combinedrate signal up to six times compared with a single sensor. …”
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Prediction of antipsychotic drug efficacy for schizophrenia treatment based on neural features of the resting-state functional connectome by Song Liu, Meng Wang, Weiyi Han, Anran Chen, Xuzhen Liu, Kang Liu, Xue Li, Yi Chen, Luwen Zhang, Qing Liu, Xiaoge Guo, Xiujuan Wang, Ning Kang, Yong Han, Yuanbo Li, Xi Su, Luxian Lv, Bing Liu, Wenqiang Li, Yongfeng Yang

“…Baseline clinical status and post-treatment outcome were assessed using the Positive and Negative Symptom Scale (PANSS), and clinical improvement was rated by the total score reduction. Based on acquired imaging data, a resting-state functional connectivity matrix was constructed for each patient, and a connectome-based predictive model was subsequently established and trained to predict individual PANSS total score reduction. …”
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Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks by N. Farhadi, A. Mardani, A. Hosainpour, B. Golanbari

“…Conversely, an increase in speed led to a reduction in rut depth, particularly during the initial pass. …”
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A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features by Xuhui ZHANG, Yunkai CHI, Yuyang DU, Junying JIANG, Wenjuan YANG, Youjun ZHAO, Jicheng WAN, Yanqun WANG, Chenhui TIAN

“…Additionally, the root mean square error (RMSE) decreased from 0.531 to 0.426, suggesting a reduction of 19.8%. …”
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Models for sustainable management of livestock waste based on neural network architectures by Anatoliy Tryhuba, Krzysztof Mudryk, Inna Tryhuba, Marian Kotsylovskyi, Dmytro Sorokin, Olena Bezaltychna, Pawel Pysz, Taras Hutsol

“…The optimized MLP model demonstrated high predictive performance, achieving a mean squared error (MSE) of 0.0005 and a mean absolute percentage error (MAPE) of 6.51%, compared to 8.01% for the baseline model. …”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…Data preparation processes include noise reduction, spatial interpolation, and addressing missing data. …”
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Data driven healthcare insurance system using machine learning and blockchain technologies by Irum Matloob, Shoab Khan, Bushra Bashir, Rukaiya Rukaiya, Javed Ali Khan, Hessa Alfraihi

“…The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. …”
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Evaluation of Four Semi‐Mechanistic Models for Predicting Glycemic Control With a Glucagon Receptor Antagonist in People With Type 2 Diabetes by Austin Yue Feng Tan, Karen Schneck, Parag Garhyan, Eric Chun Yong Chan, Lai San Tham

“…Metrics for predictive performance included: (a) mean change from baseline HbA1c (ΔHbA1c) at Week 24 between observed and simulated values; (b) mean prediction error (MPE) for bias; and (c) root mean squared error (RMSE) for precision. …”
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Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks by Ghazi Gharsallah, Georges Kaddoum

“…Compared to baseline methods—namely, a classical LS-LMMSE approach and a wireless-based model that solely relies on channel measurements—our framework achieves up to a 30.82% reduction in mean squared error (MSE) and a 32.76% increase in goodput. …”
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Tibiofemoral Joint Contact Force Estimation Based on OpenSim Musculoskeletal Modeling by WANG Xiaoling, JIAN Jiawei, XIE Qiurong, LIAN Zhanghui, GUO Chunming, GUO Jiemei, LI Yurong

“…The root mean square error of the estimated and measured peak contact force of the medial and lateral sides were 0.43±0.25 BW and 0.34±0.24 BW, and the errors at the time of peak contact force onset were 44.09±34.66 ms and 67.52±61.19 ms. …”
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