Magnetic Particle-Based Automated Chemiluminescence Immunoassay for the Determination of Hydrocortisone Residues in Milk by Yuan-Yuan Yang, Bao-Zhu Jia, Zhen-Lin Xu, Yi-Xian Liu, Lin Luo

“…Automated operations reduce human error and enhance the accuracy and repeatability of tests. …”
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Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System by Nasrettin Koksal, AbdulRahman Ghannoum, William Melek, Patricia Nieva

“…The developed OM system achieves a range between 96.6% and 97.3% classification accuracy for all tested ML models, where the error translates to a minor delay in the time in which an individual’s location is classified, introducing a highly reliable indoor/outdoor tracking system.…”
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Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach by s Manjunatha, Khalil Ur Rehman, Wasfi Shatanawi, Tanuja T N

“…Further, the correlation between the parameter on the rate of heat transfer is analysed by using Mean square error and regression graphs. The key outcome of this research is that, the temperature upsurges by increasing the solid volume of nanoparticle due to higher thermal conductivity of the nanoparticles. …”
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Evaluation of Photoplethysmography-Based Monitoring of Respiration Rate During High-Intensity Interval Training: Implications for Healthcare Monitoring by Marjolein Muller, Kambiz Ebrahimkheil, Tara Vijgeboom, Casper van Eijck, Eelko Ronner

“…The PPG algorithm for RR monitoring showed an average root mean square (Arms) error of 2.13 breaths per minute (brpm), a bias of −0.09 brpm, and limits of agreement (LoA) from −4.28 to 4.09 brpm. …”
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Ship path planning and tracking based on rapidly exploring random tree star and convex optimization by Chang ZHOU, Te YU, Jiapeng LIU, Dihua LU, Qingshan ZENG

“…ResultsSimulation results demonstrate that using this algorithm, ships can achieve efficient path planning and trajectory tracking, with path search time less than 2×10−5 s, path optimization time less than 0.5 seconds, and trajectory tracking absolute error less than 0.75 m. ConclusionsSimulation results indicate that the proposed path planning and tracking algorithm ensures effective path search and optimization for ships, providing a foundation for further research and industrial applications of autonomous surface vehicles.…”
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Nonlinear dynamics of laminar flow in porous channels: Effects of wall dilation and inertial–viscous interplay by Mahmmoud M. Syam, M. Alabdul Razzak, Hossam R. Ammar, Sally Suhel Aldeleh

“…The method achieved an L2-truncation error on the order of 10−14, outperforming traditional numerical solvers such as BVP4C, RKF45, and VIM. …”
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A method of urban high-precision DEM construction based on point cloud model by Weibo Zeng, Xiaoxiao Zhang, Shuangxi Gu, Ran Zhang, Lele Yu, Yijun Zhou, Yuqing Zhuo

“…The experimental results demonstrate that the proposed method in this paper exhibits superior performance in terms of point cloud filtering accuracy, DEM construction error, efficiency, and method adaptability when compared to traditional elevation grid, ordinary Kriging model, and other methods.…”
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Filtering airborne LiDAR data based on multi-view window and multi-resolution hierarchical cloth simulation by Shangshu Cai, Yong Pang, Sisi Yu, Xiangguo Lin, Xinlian Liang

“…Results showed that the proposed algorithm outperformed the existing algorithms, with the lowest average total error of 3.85% and the highest average kappa coefficient of 87.75%. …”
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Experimental Analysis of Accuracy and Precision in Displacement Measurement Using Millimeter-Wave FMCW Radar by Hajime Takamatsu, Nariteru Hinohara, Ken Suzuki, Fuminori Sakai

“…The experiments demonstrated a maximum accuracy error of +0.359 degrees (1.907 μm displacement) and a maximum 3-sigma precision of ±0.358 degrees (±1.180 μm displacement), validating the feasibility of using millimeter-wave radar to measure very small displacements.…”
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Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings by Phu-Anh-Huy Pham, Nhat-Duc Hoang

“…Additionally, an asymmetric squared error loss function is utilized to reduce overestimations by 12 %. …”
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Physics informed neural networks for fluid flow analysis with repetitive parameter initialization by Jongmok Lee, Seungmin Shin, Taewan Kim, Bumsoo Park, Ho Choi, Anna Lee, Minseok Choi, Seungchul Lee

“…This strategy effectively simulated vortex and shear layers and achieved the lowest mean square error in both cases. Furthermore, principal component analysis confirmed its capability to dynamically modulate model parameters during the training process. …”
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Numerical Simulation Analysis of Concrete-Filled Circular CFRP–Steel Middle Long Columns by Chuheng Zhong, Shuai Wang, Jun Leng, Jinzhi Zhou

“…Moreover, the error of ANSYS calculation results is small, which is in line with the test. …”
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Load frequency control resilience of hybrid power system with renewable energy sources and superconducting magnetic energy storage using FO-Fuzzy-PID controller by Misagh Jalilian, Abdollah Rastgou, Saeed Kharrati, Saman Hosseini-Hemati

“…In other Scenario, it achieved the lowest integral of time-weighted absolute error (ITAE) value of 0.0061, representing a 44.5% improvement over the PSO-PID controller's ITAE of 0.11. …”
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Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net by Darong Zhu, Ziyan Sun, Fangbin Wang

“…The reconstruction performance is further optimized by combining perceptual loss and mean squared error loss. Experimental results indicate that, for 2x degraded images, the proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) value exceeding 41 dB, outperforming other methods, with the Structural Similarity Index (SSIM) reaching 0.9872. …”
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Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys by Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su

“…Moreover, these new numerical descriptions for phase classification can be directly applied to regression model predictions of HEAs, reducing the error by 22% and improving the R 2 value from 0.79 to 0.88 in hardness prediction. …”
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A Prognostic Model for Newly Operated Highway Bridges Based on Censored Data and Survival Analysis by Liang Huang, Jixin Duan, Shizhan Xu, Jiapeng Zhu, Jun Liu, Danjie Niu, Li Xu, Jiangtao You, Pengtao Xue

“…To further verify the accuracy of the model, the authors built a multilayer perceptron neural network with one hidden layer and used the cross-entropy error as the loss function. It showed that the importance of the deck system, superstructure, and substructure to the decay of the bridge structure gradually decreased. …”
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Inverse Gravimetric Problem Solving via Prolate Ellipsoidal Parameterization and Particle Swarm Optimization by Ruben Escudero González, Zulima Fernández Muñiz, Antonio Bernardo Sánchez, Juan Luis Fernández Martínez

“…The best-fitting model includes four ellipsoids (two low- and two high-density), reproducing the main features of the observed Bouguer anomaly with a prediction error of 20–25%. The inferred geometry suggests that fluid migration is controlled by fault-related damage zones rather than shallow reservoirs. …”
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A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification by Zhenhua Zhang, Guojuan Xu, Liangjun Dai, Tao Cheng, Banglu Xi, Mingliang Chen, Jiaqiang Yang

“…In addition, the bearing capacities of the theory differ very little from the experimental and previous results, with a max error of nearly 10%. This study confirms that the proposed model reliably captures the effects of anchor inclination, providing valuable insights for designing inclined anchors in engineering practice.…”
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Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method by Fengmin Hu, Beibei Yang, Zushuai Wei, Changlu Cui, Lingkui Meng

“…At the grid scale, the LSTM-BCNN model achieves a correlation coefficient (CC) of 0.89 ± 0.06, Nash-Sutcliffe efficiency coefficient (NSE) of 0.78 ± 0.12, and normalized root mean square error (NRMSE) of 0.11 ± 0.02. Across the 40 basins, the LSTM-BCNN model effectively reconstructed the TWSA data during the gap period.…”
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Modeling Sea Surface Temperature Variability with Meteorological and Water Quality Indicators Using VAR and Prophet Forecasting Models by L. Elneel, N. A. Albakri, M. S. Zitouni, S. Almansoori, H. Al-Ahmad

“…The Prophet and VAR models yielded Root Mean Square Error (RMSE) values of 0.82 and 0.93, respectively. …”
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