Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device by Guangxiang Yang, Hua Liang

“…The experimental result shows a maximum data error reduction of 7.5% is obtained and SNRs (signal-to-noise ratio) of rotation speed and catching torque are improved for 3.9% and 6.4%, respectively.…”
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Adaptive pilot design for OFDM based on deep reinforcement learning by Qiaoshou LIU, Xiong ZHOU, Shuang LIU, Yifeng DENG

“…For orthogonal frequency division multiplexing (OFDM) systems, an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process, where the index of pilot positions was defined as actions.A reward function based on mean squared error (MSE) reduction strategy was formulated, and deep reinforcement learning was employed to update the pilot positions.The pilot was adaptively and dynamically allocated based on channel conditions, thereby utilizing channel characteristics to combat channel fading.The simulation results show that the proposed algorithm has significantly improved channel estimation performance compared with the traditional pilot uniform allocation scheme under three typical multipath channels of 3GPP.…”
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Optimizing the Smart Pump Drug Library Update Process: An Ongoing Effort at an Academic Medical Center by Courtney Olson, Carin Bouchard

“…Smart infusion pumps with dose-error reduction software and customizable drug libraries have increased the safety of intravenous infusion delivery; however, maintaining the most updated drug library is a multistep process for most infusion pumps, which requires effort and engagement by the end user to activate the latest drug library. …”
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Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990–2021 by Zinabu Bekele Tadese, Fetlework Gubena Arage, Tigist Kifle Tsegaw, Eyob Akalewold Alemu, Tsegasilassie Gebremariam Abate, Eliyas Addisu Taye

“…Model performance was evaluated using Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and Symmetric Mean Absolute Percentage Error. …”
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Research on the evolution and prediction of the heights of water-conducting fracture zones in overlying rocks during layered mining of extremely thick coal seams by MENG Hailun, CHENG Xianggang, QIAO Wei

“…As the working face continued to advance, the horizontal fractures within the overlying rock layers were compacted by the layer above, the fracture aperture decreased, and the fractal dimension gradually reduced. ③ During layered mining, the fractal dimension of fractures generally exhibited four stages: ascending dimension stage, dimension reduction stage, stationary stage, and fluctuating stage. ④ The PSO-SVR model was evaluated using indicators including mean absolute error (MAE), mean bias error (MBE), and correlation index R2. …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…The application of GWO for hyperparameter tuning has resulted in a 37.3% reduction in root mean square error (RMSE), a 37.4% drop in mean absolute percentage error (MAPE), and a 2.06% improvement in <inline-formula> <tex-math notation="LaTeX">$\text {R}^{2}$ </tex-math></inline-formula> to improve the precision of prediction. …”
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Harmonizing remote sensing and ground data for forest aboveground biomass estimation by Ying Su, Zhifeng Wu, Xiaoman Zheng, Yue Qiu, Zhuo Ma, Yin Ren, Yanfeng Bai

“…The effectiveness of this approach was demonstrated by a 0.67 increase in the correlation coefficient R2, a 43.57 % reduction in the root mean square error (RMSE), and a 68.00 % reduction in the mean square error (MSE) achieved through the optimal combination of data sources. …”
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Consistency of the orbital chronologies derived for Vostok and EPICA DC ice cores based on the dependence of ice air content on local insolation by V. A. Khomyakova, N. A. Tebenkova, V. Ya. Lipenkov, D. Raynaud

“…Comparison of the TAC timescales with the optimized chronologies AICC2012 and AICC2023 for the Vostok and EDC cores showed that their discrepancy, as a rule, does not exceed 2 ka, which is consistent with both the standard error of the TAC­based dating method (±2.1 ka) and the standard errors of the AICC2012 (±1.9…4.8 ka) and AICC2023 (±0.8…2.6 ka) reference chronologies themselves. …”
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Three-dimensional visualization of maize roots based on magnetic resonance imaging by Fang Xiaorong, Wang Nanfei, Zhang Jianfeng, Gong Xiangyang, Liu Fei, He Yong

“…Similarly, actual geometric parameters of the samples were measured manually by a vernier caliper and water displacement method.By comparing the reconstruction model of root architecture with the physical object, it was found that the obtained models were well consistent with real samples, showing very good agreement in shape, volume and other morphological parameters, and the errors among them were less than 3%.In sum, the presented methodology can avoid making great efforts in experimental measurements and consequently development of the root architecture models, and decrease the error generated from manual data extraction. …”
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The Effects of Presenting AI Uncertainty Information on Pharmacists’ Trust in Automated Pill Recognition Technology: Exploratory Mixed Subjects Study by Jin Yong Kim, Vincent D Marshall, Brigid Rowell, Qiyuan Chen, Yifan Zheng, John D Lee, Raed Al Kontar, Corey Lester, Xi Jessie Yang

“…A pronounced “negativity bias” was observed, where the degree of trust reduction when the AI made an error exceeded the trust gain when the AI made a correct decision (z=–11.30; P<.001). …”
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Devaluing memories of reward: a case for dopamine by Benjamin R. Fry, Nicolette Russell, Victoria Fex, Bing Mo, Nathan Pence, Joseph A. Beatty, Fredric P. Manfredsson, Brandon A. Toth, Christian R. Burgess, Samuel Gershman, Alexander W. Johnson

“…Abstract Midbrain dopamine cells encode differences in predictive and expected value to support learning through reward prediction error. Recent findings have questioned whether reward prediction error can fully account for dopamine function and suggest a more complex role for dopamine in encoding detailed features of the reward environment. …”
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The Role of Landscape Metrics and Spatial Processes in Performance Evaluation of GEOMOD (Case Study: Neka River Basin) by Shrif Joorabian Shooshtari, Kamran Shayesteh, Mehdi Gholamalifard, Mahmood Azari, Juan Ignacio López-Moreno

“…In terms of this index, the model generates good agreement between reference and observed maps in 2001, and 2010 with relative error values of 5 %, and 0.4 %. Spatial process was attrition in the ground truth data due to the reduction in area, and number of patch per unit area during 1984–2001, and 2001–2010. …”
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TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region by Jie Lian, Sirong Huang, Jiahao Shao, Peiyan Chen, Shengming Tang, Yi Lu, Hui Yu

“…Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6% and 33.3%, respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). …”
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Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model by Meriem Ben Lazreg, Sabeur Jemmali, Bilal Manai, Mahmoud Hamouda

“…Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP‐EKF, and 0.82% with the FP‐SVSF, compared to the same algorithms without the proposed FP method. …”
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Enhanced Inversion of Sound Speed Profile Based on a Physics-Inspired Self-Organizing Map by Guojun Xu, Ke Qu, Zhanglong Li, Zixuan Zhang, Pan Xu, Dongbao Gao, Xudong Dai

“…The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. …”
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A novel adaptive PID controller with new seesaw algorithms using alternative derivatives by Juan Pablo Manzo Hernández, Julio César Solano Vargas, Juan Manuel Barrera Fernández, Daniel Moreno Orduña, Cristian Hamilton Sánchez Saquín

“…These behaviours can be inserted into the [Formula: see text] to imbue it with adaptive characteristics, achieving adaptive [Formula: see text] ([Formula: see text]) controllers with faster signal rise and stabilization times, reduction of the maximum peak, and less error accumulation compared to conventional [Formula: see text].…”
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Nonlinear time domain and multi-scale frequency domain feature fusion for time series forecasting by Kejiang Xiao, Yefeng Li, Yaning Dong, Wenqi Yang, Binting Yao, Liang Chen

“…Experimental results show that the WTConv-iKransformer achieves an additional 3% error reduction compared with individual enhanced models and realizes an average 25% error decrease over mainstream methods (e.g., Informer, LSTM) on ETTh1, ETTm1, Electricity, and Traffic datasets.…”
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Noise Processing Method of MEMS Tilt Sensor Using Improved Kalman Filter Based on Quantum Particle Swarm Optimization by Yutong Ge, Weizheng Ren, Kaile Yu, Yiran Zhang, Yuxiao Li

“…According to the experimental findings, the proposed method exhibits a superior noise reduction effect, as evidenced by its smaller mean absolute error (MAE) and mean square error (MSE) compared to alternative techniques such as variational mode decomposition (VMD) combined with wavelet transform (WT), back propagation (BP) neural network optimized Kalman filter and particle swarm optimization (PSO) improved Kalman filter. …”
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BER and PSD Improvement of FBMC with Higher Order QAM Using Hermite Filter for 5G Wireless Communication and beyond by Hise Teferi Dumari, Demissie Jobir Gelmecha, Rajeev K. Shakya, Ram Sewak Singh

“…The performances of each multicarrier technique are analyzed based on power spectral density and bit error rate. Simulation result shows that the power spectral density of FBMC with QAM using Hermite filter resulted in 4.7 dB reduction of out of band emission compared to FBMC with QAM using PHYDYAS filter. …”
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An adaptive continuous threshold wavelet denoising method for LiDAR echo signal by Dezhi Zheng, Tianchi Qu, Chun Hu, Shijia Lu, Zhongxiang Li, Guanyu Yang, Xiaojun Yang

“…The adaptive threshold is dynamically adjusted according to the wavelet decomposition level, and the continuous threshold function ensures continuity with lower constant error, thus optimizing the denoising process. Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error (RMSE) compared with other algorithms. …”
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