Semantic SLAM using laser-vision data fusion: Enhancing autonomous navigation in unstructured environments by Ning Chen, Dong Wei, Dongsheng Lin, Linhan Lin

“…Additionally, the size error between the generated map and the actual scene was reduced to just 0.81 %, indicating high fidelity in mapping accuracy. …”
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An ensemble-driven machine learning framework for enhanced water quality classification by Preet Singh, Taniya Hasija, Salil Bharany, Hafiza Nazra Tun Naeem, B. Chinna Rao, Seada Hussen, Ateeq Ur Rehman

“…The soft voting ensemble model, therefore, shows a relative improvement of 1.46% in accuracy over the best-performing base learner and a 27.8% reduction in its error rate. It is, therefore, confirmed that ensemble learning soft voting improves the reliability of water quality classification as well as accuracy, thus providing a strong platform for future environmental monitoring systems.…”
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Financial Policies and Corporate Income Tax Administration in Nigeria by Cordelia Onyinyechi Omodero, Joy Limaro Yado

“…This investigation applies autoregressive distributed lag and error correction models, acknowledging the existence of a long-term relationship within the series. …”
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Improving Georeferencing Accuracy in Drone Imagery: Combining Drone Camera Angles with High and Variable Fields of View by Vishal Nagpal, Manoj Devare

“…The proposed approach further shows a quantitative improvement of 12.50% to 75.0% in the geolocation error reduction claimed. It was achieved by the decrease of MAE from 0.108 km to 0.055 km while RMSE was lowered from 0.111 km to 0.057 km indicating the reliability of the method. …”
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Enhancing weather index insurance through surrogate models: leveraging machine learning techniques and remote sensing data by Sachini Wijesena, Biswajeet Pradhan

“…The GLM achieved a mean absolute error (MAE) of 8.2%, which is comparable to the neural network model’s MAE of 7.6%. …”
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Accelerated Modeling of Transients in Electromagnetic Devices Based on Magnetoelectric Substitution Circuits by Sergii Tykhovod, Ihor Orlovskyi

“…The simulation of processes over a long time interval demonstrate error reduction and stabilization. This indicates the potential of the proposed method for simulating processes in more complex electromagnetic devices, (for example, transformers).…”
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The impact of study size on meta-analyses: examination of underpowered studies in Cochrane reviews. by Rebecca M Turner, Sheila M Bird, Julian P T Higgins

“…In the subset examined, odds ratios in underpowered studies were 15% lower (95% CI 11% to 18%, P<0.0001) than in adequately powered studies, in meta-analyses of controlled pharmacological trials; and 12% lower (95% CI 7% to 17%, P<0.0001) in meta-analyses of controlled non-pharmacological trials. The standard error of the intervention effect increased by a median of 11% (inter-quartile range -1% to 35%) when underpowered studies were omitted; and between-study heterogeneity tended to decrease.…”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Field validation showed that the productivity prediction model achieved an average error of 7.06%, providing a basis for horizontal fracture engineering design and achieving cost reduction and efficiency improvement in oilfield development.…”
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Tribological Performance of Electrochemically Textured EN-GJS 400-15 Spheroidal Cast Iron by Peng Jiang, Jonathon Mitchell-Smith, John Christopher Walker

“…Experimental results demonstrated that EJM textured surfaces were accurately fabricated within a ±2.50 µm standard error in depth, with chemical etching effects reducing the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>q</mi></mrow></msub></mrow></semantics></math></inline-formula> roughness of initial grinding marks by 0.223 µm. …”
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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. …”
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Separating the albedo-reducing effect of different light-absorbing particles on snow using deep learning by L.-A. Chevrollier, A. Wehrlé, J. M. Cook, N. Pirk, L. G. Benning, A. M. Anesio, M. Tranter

“…The inversion method was applied to 180 ground field spectra collected on snowfields in southern Norway, with a mean absolute error on spectral albedo of 0.0056, and surface parameters that closely matched expectations from qualitative assessments of the surface. …”
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Investigating the Capabilities of Ensemble Machine Learning Model in Identifying Near-Fault Pulse-Like Ground Motions by Jafar Al Thawabteh, Jamal Al Adwan, Yazan Alzubi, Ahmad Al-Elwan

“…These models offer superior performance in handling large datasets, noise reduction, and feature extraction, making them ideal for this application. …”
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Combustion Control of Ship’s Oil-Fired Boilers Based on Prediction of Flame Images by Chang-Min Lee

“…Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.…”
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Study on Multiobjective Path Optimization of Plant Protection Robots Based on Improved ACO‐DWA Algorithm by Jing Niu, Chuanyan Shen, Lipeng Zhang, Guanghao Gao, Jiahao Zheng

“…In the orchard real‐time experiment, compared with other excellent algorithms, the navigation distance error is reduced obviously. These experimental results show that this method would obviously improve the robot access effect in the inner area between the tree row, significantly meet the requirements of high safety and speed level of robots in these scenes. …”
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Impact of PM_2.5 Pollution on Solar Photovoltaic Power Generation in Hebei Province, China by Ankun Hu, Zexia Duan, Yichi Zhang, Zifan Huang, Tianbo Ji, Xuanhua Yin

“…The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. …”
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Urban Fire Spatial–Temporal Prediction Based on Multi-Source Data Fusion by Haiyu Xiang, Lizhi Wu, Zidong Guo, Shaoyun Ren

“…Extensive evaluations on real-world datasets from Anci and Guangyang Districts demonstrate that UFSTP achieves a 16.2% average reduction in Mean Absolute Error (MAE) for time prediction and a 3.3% average improvement in top-1 hit rate for regional prediction over state-of-the-art baselines. …”
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Distributed Relative Pose Estimation for Multi-UAV Systems Based on Inertial Navigation and Data Link Fusion by Kun Li, Shuhui Bu, Jiapeng Li, Zhenyv Xia, Jvboxi Wang, Xiaohan Li

“…The experimental results demonstrate that the system achieves a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>76</mn><mo>%</mo></mrow></semantics></math></inline-formula> reduction in positioning error compared to using data link measurements alone. …”
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A fast and fully automated system for segmenting retinal blood vessels in fundus images by Alqassab Ans Ibrahim Mahameed, Luque-Nieto Miguel-Ángel

“…Manual segmentation is time-consuming and prone to human error, leading to inconsistent results. Additionally, existing automated methods often struggle with low-quality images or variations in illumination, hindering their effectiveness. …”
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A Fast Multi-Scale of Distributed Batch-Learning Growing Neural Gas for Multi-Camera 3D Environmental Map Building by Chyan Zheng Siow, Azhar Aulia Saputra, Takenori Obo, Naoyuki Kubota

“…The experimental results show that the proposed method can extract topological features enough to integrate point cloud data sets, and it runs 14 times faster than the previous GNG method with a 23% reduction in the quantization error. Finally, this paper discuss the advantage and disadvantage of the proposed method through numerical comparison with other methods, and explain future works to improve the proposed method.…”
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Experimental and simulation study on fracture conductivity of acid-fracturing in Dengying Formation of Sichuan Basin by CHEN Xiang, WANG Guan, LIU Pingli, DU Juan, WANG Ming, CHEN Weihua, LI Jinlong, LIU Jinming, LIU Fei

“…As closure stress increases, different acids and combinations exhibit distinct patterns of conductivity reduction, with potential for two rapid decline phases. …”
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