Psychometric testing of the Persian version of the nurse caring behavior scale by Mojtaba Jafari, Reza Ghanei Gheshlagh, Abbas Ebadi, Asra Nassehi, Alessandro Sili, Michela Piredda

“…Confirmatory factor analysis was conducted to assess the scale dimensionality using multiple fit indices such as the root mean square error of approximation (RMSEA), the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the standardized root mean square residual (SRMR). …”
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Evaluation of the Possibility of Calculating LD50 and LD10 Using a Modified Script in the R Environment by P. V. Shadrin

“…The regression analysis confirmed the accuracy of the MS-based LD50 and LD10 calculations, which was demonstrated by a statistically insignificant systematic error, a significant dose dependence at P=0.999, and a high coefficient of determination (R2). …”
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LLM4TDG: test-driven generation of large language models based on enhanced constraint reasoning by Jingqiang Liu, Ruigang Liang, Xiaoxi Zhu, Yue Zhang, Yuling Liu, Qixu Liu

“…The metric surpasses the state-of-the-art GPT-4, with a performance of 92.16% on HUMANEVAL and 87.14% on HUMANEVAL+, which enhances the error correction and functional correctness in test-driven code generation. …”
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Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. …”
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GC-Like LDPC Code Construction and its NN-Aided Decoder Implementation by Yu-Lun Hsu, Li-Wei Liu, Yen-Chin Liao, Hsie-Chia Chang

“…To demonstrate the capability of the NN-aided vwMS algorithm with the fast-convergent shuffled scheduling method, a GC-like (9126,8197) LDPC decoder is implemented for NAND flash applications, achieving a 6.56 Gbps throughput with a core area of <inline-formula> <tex-math notation="LaTeX">$0.58~mm^{2}$ </tex-math></inline-formula> under the 40-nm CMOS TSMC process, and average power consumption of 288 mW under the frame error rate of <inline-formula> <tex-math notation="LaTeX">$2.64 \times 10^{-5}$ </tex-math></inline-formula> at 4.5dB. …”
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Analysis and Optimization Design of Internal Flow Evolution of Large Centrifugal Fans Under Inlet Distortion Effects by Shuiqing Zhou, Tianci Wang, Zijian Mao, Laifa Lu

“…The corrected SST k-ω model shows a maximum error of only 4.7%. Simulation results show that the fan inlet generates a positive pre-swirl inflow with a relative distortion intensity of 3.83°. …”
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Rolling Bearing Fault Diagnosis Method Based on SWT and Improved Vision Transformer by Saihao Ren, Xiaoping Lou

“…To further enhance generalization, the model combines cross-entropy and mean squared error loss functions. The experimental results show that the proposed method achieves average accuracy rates of 99.96% and 96.51% under constant and varying load conditions, respectively, on the Case Western Reserve University bearing fault dataset, outperforming other methods. …”
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Design of an Iterative AI-Driven Latency Prediction and QoS-Aware Task Scheduling in Mobile Edge Computing: A Federated and Reinforcement Learning Process by Sharma Garima, Khare Rashi, Kulkarni Neha, Pagare Sanjay, Tiwari Vanshika

“…The tremendous upsurge of latency-sensitive applications in Mobile Edge Computing (MEC), thus requiring an efficient task scheduler depending on precise and adaptive latency prediction. …”
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Dry Friction Behavior of Stainless Steel Kinematic Pair of Spent Fuel Shearing Machine under Thermal-mechanical Coupling by YANG He1, DONG Jianpeng2, , GAO Feng1, GAO Shan1, TAN Aoshuang2

“…Compared with the experimental results, the maximum wear prediction error is 9.34%, which verifies the effectiveness of the established model. …”

Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…Furthermore, improvements in the 3°C error threshold are observed, with VDSR and EDSR showing increases of 2.10 and 2.16%, respectively. …”
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Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS) by Ray L. Khuboni, Hongjun Xu

“…To refine depth estimation, we incorporate multi-scale depth confidence-guided geometric consistency, propagating reliable depth estimates from coarse to finer scales and minimizing propagation error. A dual bilateral weighted normalized cross-correlation is incorporated to reduce irrelevant pixel influence and improve correspondence in texture-less and repetitive regions. …”
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Interactive influence of basalt fibres and pozzolanic additives on the mechanical and microstructural properties of self-compacting concrete by Olive Sharon R P, SenthilPandian M

“…RSM validated all the experimental results with a mean error of less than 4 %, confirming the reliability of the findings. …”
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Optimization and Benchmarking of Image Segmentation for Improved Landmark Detection in Lower Limb X-Rays and Accurate Coronal Plane Alignment of the Knee Classification by Sebastian Amador Sanchez, Ashkan Zarghami, Philippe van Overschelde, Jef Vandemeulebroucke

“…The evaluation assessed the landmark detection error and phenotype classification accuracy based on lower limb alignment. …”
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Aligning Evapotranspiration from MOD16A2.061 Product to Ground Estimates in Piemonte (NW Italy): an analysis of temporal and spatial biases by E. Ronco, E. C. Borgogno Mondino

“…The corrected dataset (<em>&Ecirc;T</em>₀) shows significantly improved agreement with ground-based ET₀, reducing the Mean Absolute Error from 10.06 mm/8 d to 2.48 mm/8 d, a 75% improvement. …”
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Translation and Validation of the Premenstrual Assessment Form-Short Form Questionnaire in Hungarian by Olívia Dózsa-Juhász, Alexandra Makai, Viktória Prémusz, Pongrác Ács, Márta Hock

“…During the CFA, the three-factor structure (Affect, Water Retention, and Pain) was supported (root mean-square error approximation [RMSEA] = 0.054; Tucker–Lewis Index = 0.965; Comparative Fit Index = 0.976; χ2 = 48.642; df = 31; p = 0.023; χ2/df = 1.569). …”
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A New Method for Determining the Sampling Volume and the Number of Particles Within It for Particle Concentration Identification in Defocused Interferometric Particle Imaging by Hongxia Zhang, Ye Zhou, Jing Liu, Dagong Jia, Tiegen Liu

“…The largest experimental error for 45 &#x03BC;m particles with the concentration of 0.006 mm^&#x2212;3 is 10.4&#x0025; and decreases with the increase of the particle concentration. …”
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Silicon Photonics for Inter&#x002F;Intra-Chip Wireless Communication Using RF On-Chip Antennas by Ajaypal Singh Dhillon, Frederick Melanson, Odile Liboiron-Ladouceur

“…The intra-chip RF data transmission has been confirmed with bit- error-rate (BER) measurements of below 10^&#x2212;9 up to a data rate of 4 Gbps. …”
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A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China by Rihui Yang, Yuqing Zhong, Xiaoxiang Zhang, Aizemaitijiang Maimaitituersun, Xiaohan Ju

“…The results indicate that among the established 66 × 158 local GWR models, the coefficient of determination (R²) ranges from 0.39 to 0.88, with a root mean squared error (RMSE) of approximately 2.60 cm. The proportion of downscaling models with an R² below 0.5 was 18.52%. …”
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Fast Hadamard-Encoded 7T Spectroscopic Imaging of Human Brain by Chan Hong Moon, Frank S. Lieberman, Hoby P. Hetherington, Jullie W. Pan

“…The moderate spin echo sequence, which targets singlet compounds (i.e., N-acetyl aspartate, creatine, and choline), uses cascaded multi-slice RF excitation pulses to minimize the chemical shift dispersion error. The J-refocused sequence targets coupled spin systems (i.e., glutamate and myo-inositol) using simultaneous multi-slice excitation to maintain the same TE across all slices. …”
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All-Weather Retrieval of Total Column Water Vapor From Aura OMI Visible Observations by Jiafei Xu, Zhizhao Liu

“…In terms of root-mean-square error, it is overall reduced by 90.44&#x0025; from 56.38 to 5.39 mm and 90.19&#x0025; from 53.23 to 5.22 mm compared with GNSS and radiosonde TCWV, respectively. …”
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