Effects of blood flow restriction on motoneurons synchronization by Mansour Taleshi, Franziska Bubeck, Franziska Bubeck, Leonardo Gizzi, Ivan Vujaklija

“…As expected, during BFR application, participants reported higher level of discomfort and significant deterioration in force-tracking performance, as measured using root mean square error (RMSE). Following the BFR release, the level of discomfort, along with impaired neuromuscular performance were reduced to pre-BFR condition. …”
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Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection by Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang

“…It is clinically time-consuming and error-prone to identify seizure activities by visually scanning EEG signals. …”
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Climatic characteristics of snow water equivalent in the Perm Krai area by N. A. Kalinin, A. D. Kryuchkov, I. A. Sidorov, R. K. Abdullin, A. N. Shikhov

“…In general, the ERA5-Land reanalysis reproduces SWE in the Perm region satisfactorily. Mean relative error for SWE in March does not exceed 15 %. The average correlation coefficient between the reanalysis data and the same from the observations is 0.72 for non-forest locations and 0.83 for locations in forest. …”
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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…In particular, accuracy improved by 5% using MDBM, the false positive rate reduced by 15% with MVAE, a more than 10% improvement in the F1-score with the attentive fusion network, a 20% reduction in reconstruction error with Deep Convolutional Autoencoder (DCA), detection precision improved by 12% using Adversarially Learned Inference (ALI) and a gain of 8% in Area Under the Curve (AUC) using Deep InfoMax (DIM) operations.…”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…The mean error in impurity rates between the RF monitoring model and manual count was 6.14 %, with an average detection time of 1.43 seconds. …”
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Cancer care to the population of Russia in 2022 by A. O. Shakhzadova, V. V. Starinsky, I. V. Lisichnikova

“…Сancer care in the Russian Federation in 2022 was provided at the proper level. there is a positive trend in the early detection of cancer, which partly influences the reduction in the one-year mortality rate.…”
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Evaluating behavioral intervention technologies: Integrating human-centered design and implementation science outcomes by Shumenghui Zhai, Weichao Yuwen, Tiffany L Zyniewicz, Jennifer Sonney, Jonika Hash, Maida Chen, Teresa M Ward

“…Results The USIS model incorporates five domains: (1) user-centeredness (empathy, engagement, and equity), (2) efficiency (cost, timeliness, and rapidity), (3) feasibility (learnability, memorability, error reduction, and low cognitive load), (4) satisfaction (acceptability and appropriateness), and (5) fidelity (adoption, penetration, and sustainability). …”
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Improving Subsurface Soil Moisture Estimation Using a 2‐Dimensional Data Assimilation Framework Incorporated With a Dual State‐Parameter Scheme by Dehai Liao, Jun Niu, Taisheng Du, Shaozhong Kang

“…Under 2‐dimensional DA, the information transfer between layers was more accurately characterized, leading to overall improvements with unbiased root‐mean‐square error (ubRMSE) reductions of 0.015 and 0.005 m3 · m−3, and Kling–Gupta efficiency (KGE) increases of 0.248 and 0.067 for surface and subsurface SM, respectively, across five SM networks. …”
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PEGASUS-XL with saliency-guided scoring and long-input encoding for multi-document abstractive summarization by Rawan Alsultan, Alaa Sagheer, Hala Hamdoun, Lamya Alshamlan, Latifah Alfadhli

“…Ablation studies quantify the contribution of each component, and detailed error analysis identifies remaining issues such as factual drift and residual redundancy. …”
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Revolutionizing clean energy labs: Robotic imitation learning for efficient fabrication AI-powered electrical units assembly platform by Xi Xu, Yijun Gu, Tianyi Zhang, Jiwen Yu, Stephen Skinner

“…The energy industry, now in an era of digitization driven by computational design, is gradually moving towards automating the entire process from computational prediction to device assembly, aiming to minimize the reliance on time-consuming, manual trial-and-error validation. In this study, guided by computational density functional theory (DFT) predictions, a humanoid robotic arm, based on artificial intelligence (AI), was creatively utilized to assemble clean energy devices, solid oxide fuel cells (SOFCs). …”
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Integrated thermal and magnetic susceptibility modeling for air-motion artifact correction in proton resonance frequency shift thermometry by S. A. N. Nouwens, M. M. Paulides, J. Fölker, I. VilasBoas-Ribeiro, B. de Jager, W. P. M. H. Heemels

“…The method is experimentally validated using a dedicated phantom that enables the controlled injection of air-motion artifacts and with in vivo thermometry from a clinical hyperthermia treatment.Results We showed, using probe measurements in a heated phantom, that our method reduced the mean absolute error (MAE) by 58% compared to the state-of-the-art near a moving air volume. …”
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Parameter Optimization Design and Experimental Validation of a Header for Electric Rice Reaper Binders Employed in Hilly Regions by Jinbo Ren, Difa Bao, Zhi Liang, Chongsheng Yan, Junbo Wu, Xinhui Wu, Shuhe Zheng

“…Field validation tests demonstrated an actual stem-lifting angle of 2.44° (relative error: 3.39%) and a header loss rate of 0.59%, representing a 49.6% reduction compared with the pre-optimized design. …”
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ITap: Index Finger Tap Interaction by Gaze and Tabletop Integration by Jeonghyeon Kim, Jemin Lee, Jung-Hoon Ahn, Youngwon Kim

“…The findings revealed that ITap outperformed gaze + pinch in terms of object selection speed and error reduction, particularly in scenarios involving distant or densely arranged objects. …”
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Factors Related to Superior and Inferior Hemifield Defects in Primary Open-Angle Glaucoma by Remi Takeuchi, Nobuko Enomoto, Kyoko Ishida, Ayako Anraku, Goji Tomita

“…Clinical data including visual acuity, refractive error, disc hemorrhage, VF indexes, and medical history were recorded. …”
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Impact of CO2-water-coal on enhanced coalbed methane recovery by CO2 injection in Huainan coalfield by Helong ZHANG, Shiqi LIU, Yuchen TIAN, Wenkai WANG, Shuxun SANG, Sijian ZHENG, Bing LI, Yongchun CHEN

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Kendallknight: An R package for efficient implementation of Kendall's correlation coefficient computation. by Mauricio Vargas Sepulveda

“…The kendallknight package, following Knight (1966) and posterior literature, reduces the time complexity resulting in drastic reductions in computation time, transforming operations that would take minutes or hours into milliseconds or minutes, while maintaining precision and correctly handling edge cases and errors. …”
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A deep learning framework for prediction of crop yield in Australia under the impact of climate change by Haydar Demirhan

“…The reductions in average root mean squared error are 19%, 25%, 37%, and 29% over the benchmark methods. …”
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Impact of Virtual Synchronous Generators on Frequency-Constrained Unit Commitment: A Neural Network-Assisted Framework for Assessing Rate of Change of Frequency by Ryosuke Shikuma, Yu Fujimoto, Dai Orihara, Hiroshi Kikusato, Hisao Taoka, Yasuhiro Hayashi

“…Even after worst-case compensation for NN’s ROCOF prediction errors, NN-UC still achieves net SG capacity reductions in most scenarios. …”
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Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer by Yingqiang Qi, Shuiliang Luo, Song Tang, Jifu Ruan, Da Gao, Qianqian Liu, Sheng Li

“…This model is applied to the Moxi gas field in the Sichuan Basin, using conventional logging curves as input feature variables for porosity prediction. Root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²) are used as evaluation metrics for comprehensive analysis and comparison. …”
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The relationship between shift work, circadian rhythms, and cognitive function in ICU nursing by Saeideh Moosavi, Mehran Ghalenoei, Mohammad Amerzadeh, Ali Safari Variani

“…The rhythm stability and amplitude could serve as predictive indicators for staffing shift work systems to prevent errors and enhance the system’s efficiency.…”
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