Behavioural alterations induced by chronic exposure to 10 nm silicon dioxide nanoparticles by Bashir Jarrar, Amin Al‐Doaiss, Ali Shati, Mohammed Al‐Kahtani, Qais Jarrar

“…Treated mice demonstrated anxiety‐like effect, depression tendency, behavioural despair stress, exploration and locomotors activity reduction with error induction in both reference and working memories. …”
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Assessing the Reliability of Predicted Decadal Surface Temperatures in Southeast Asia by Dara Kasihairani, Rahmat Hidayat, Supari Supari

“…The metrics of Anomaly Correlation Coefficient (ACC) and Mean Error (ME) are employed to assess the model outputs, with 51 hindcast datasets spanning initial years from 1960 to 2010 and ERA5 reanalysis data serving as the reference. …”
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UAV-based inspection of wind turbine blade surface defects detection technology by TAN Xingguo, ZHANG Gaoming

“…The feature information of defect is separated and extracted through image foreground segmentation and threshold processing, and the connected domain is framed to realize the detection of blade surface. The accuracy and error rate of defect images is calculated and tested by introducing performance evaluation index MIoU. …”
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NUMERICAL MODELING OF SWASH ZONE MORPHOLOGICAL PROCESSES IN COARSE-GRAINED BEACHES WITH XBEACH OPEN-SOURCE MODEL by S. Maleki Taghiabad, M. Adjami, A. Ahmadi

“…The results of this research indicate that the XBeach model has an acceptable performance in modeling hydrodynamic and morphodynamic processes in the Swash region, and simulation with the NH module performs better compared to the SB module (with a reduction in modeling error of over in various models). …”
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Impact of worklist selection on point-of-care ultrasound workflow – a quality improvement project by Jonathan Rowland, Jessa Baker, Natassia Dunn, Matthew Whited, Soheil Saadat, J. Christian Fox

“…Implementation also resulted in a 36% reduction in database studies containing an MRN data entry error. …”
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An adaptive non-equidistant grey model with four parameters and its applications in deformation monitoring by Shuang Yang, Changchun Li, Jiao Fu, Huanqin Ma

“…Third, the optimal selection of initial value with the minimum relative error sum of squares as the objective function further enhances the model optimization. …”
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Ensemble learning methods with single and multi-model deep learning approaches for cephalometric landmark annotation by S. Rashmi, S. Srinath, R. Rakshitha, B. V. Poornima

“…The ensemble meta-model further boosts accuracy to 83.61% and 95.4%, respectively, reducing mean radial errors by 0.38 mm and 0.33 mm. These results highlight significant improvements in accuracy and error reduction through strategic combinations of deep learning architectures and ensemble techniques demonstrating the ability to significantly enhance cephalometric landmark annotation accuracy, which is critical for the practical applicability of the methodology.…”
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A reversible and robust hybrid image steganography framework using radon transform and integer lifting wavelet transform by B. M. El-den, Walid Raslan

“…The system exhibits exceptional robustness, with a Bit Error Rate (BER) as low as 0.0017 under scaling distortions, representing a 30% reduction compared to state-of-the-art methods. …”
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Prediction of pressure drop in heavy oil water ring based on modified two fluid model by Jiqiang Fu, Mingjun Du, Jiaqiang Jing, Huichao Liu, Jie Sun, Weicong Chen, Yongjiu Chen

“…The comprehensive Reynolds number expression of eccentric water ring can effectively reflect the influence of eccentric effect on shear stress of water wall and the calculation error is less than 20% by predicting the pressure drop of the generalized eccentric water ring with different density differences.…”
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A neural network-based synthetic diagnostic of laser-accelerated proton energy spectra by Christopher J. G. McQueen, Robbie Wilson, Timothy P. Frazer, Martin King, Matthew Alderton, Ewan F. J. Bacon, Ewan J. Dolier, Thomas Dzelzainis, Jesel K. Patel, Maia P. Peat, Ben C. Torrance, Ross J. Gray, Paul McKenna

“…Trained on data from fewer than 700 laser-plasma interactions, the model achieves an error level of 13.5%, and improves with more data. …”
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Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning by Laor Boongasame, Jirapond Muangprathub, Karanrat Thammarak

“…This paper presents Laor Initialization, an innovative weight initialization technique for deep neural networks that utilizes forward-pass error feedback in conjunction with k-means clustering to optimize the initial weights. …”
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Synchronization and demodulation of LoRa null port data by Lei Fang, Chen Bo, Lv Jingzhao, Li Pingan, Du Haitao, Li Su

“…The code rate is 4/5 with the best effect error correction, and the noise resistance performance improves by about 1 dB.…”
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Speech Intelligibility Prediction Using Binaural Processing for Hearing Loss by Xiajie Zhou, Candy Olivia Mawalim, Masashi Unoki

“…Experimental results show that, compared to the baseline system of the second Clarity Prediction Challenge (CPC2) dataset, the proposed method achieves an 8.3% reduction in root mean squared error (RMSE). Notably, the proposed method reduces RMSE by 12.8% when predicting inconsistent hearing loss compared to listeners with consistent hearing levels, confirming the potential of combining hearing loss modeling with binaural processing.…”
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Phishing suspiciousness in older and younger adults: The role of executive functioning. by Brandon E Gavett, Rui Zhao, Samantha E John, Cara A Bussell, Jennifer R Roberts, Chuan Yue

“…A logistic regression, which accounted for a 22.7% reduction in error variance compared to the null model and predicted phishing suspiciousness with 73.1% (95% CI [66.0, 80.3]) accuracy, revealed three statistically significant predictors: the main effect of education (b = 0.58, SE = 0.27) and the interactions of age group with prior awareness of phishing (b = 2.31, SE = 1.12) and performance on the Neuropsychological Assessment Battery Mazes test (b = 0.16, SE = 0.07). …”
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Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology by Tian-Bing Ma, Qiang Wu, Fei Du, Wei-Kang Hu, Yong-Jing Ding

“…Results show that the improved algorithm in our study has the best threshold segmentation effect, the error classification can be close to 0.0003, and the minimum deviation of the obtained vibration displacement is close to 0.1 pixels, which can realize the accurate extraction of the vibration signal of the vertical shaft tank. …”
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A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm by Haifeng Li, Tao Jin, Xian Xu, Lin Shi

“…The algorithm demonstrates fast convergence, yielding solutions in less than 0.6 s across 14 repeated experiments, with an average convergence time reduction of 42% compared to traditional algorithms. …”
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NetCodeAIoT: Enhancing Augmented Intelligence of Things for Vehicle Systems in 5G Networks by Okuwudili Mathew Ugochukwu, Renata Lopes Rosa, Muhammad Saadi, Demostenes Z. Rodriguez, Frederico G. Guimaraes

“…NetCodeAIoT dynamically adjusts the sparsity level of the decoding matrix, implements unequal error protection network coding, and enables instantaneous decoding of data. …”
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Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks by Jehan Esheh, Sofiene Affes

“…By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). …”
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An improved small object detection CTB-YOLO model for early detection of tip-burn and powdery mildew symptoms in coriander (Coriandrum sativum) for indoor environment using an edge... by Parwit Chutichaimaytar, Zhang Zongqi, Kriengkri Kaewtrakulpong, Tofael Ahamed

“…Early and accurate detection of these symptoms is critical for maintaining yield and quality, yet traditional visual inspection methods are subjective and prone to error. To address this, we developed an enhanced deep learning model, Coriander Tip-Burn YOLO (CTB-YOLO), specifically tailored for detecting small-object symptoms in coriander leaves, emphasizing the reduction of false-positive detections (FP), which could lead to erroneous alerts and unnecessary interventions. …”
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Significant Influence on Residual Bending Strength by Cracks Generation During Grinding of Ceramics by Jinshuo Zhang, Tianyi Sui, Bin Lin, Bingrui Lv, Jingming Li, Jingguo Zhou

“…Lastly, a model is developed to delineate the relationship between processing parameters and the residual bending strength of the product, with the model exhibiting an error margin of less than 11%. This model clearly reveals the effect of crack generation under different process parameters on residual flexural strength.…”
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