Enhancing cardiovascular monitoring: a non-linear model for characterizing RR interval fluctuations in exercise and recovery by Matías Castillo-Aguilar, Diego Mabe-Castro, David Medina, Cristian Núñez-Espinosa

“…Validation against real-world RRi data demonstrated robust model performance (R 2 = 0.868, CI95%[0.834, 0.895] and Root Mean Square Error [RMSE] = 32.6 ms, CI95%[30.01, 35.77]), accurately reflecting autonomic recovery and exercise-induced fluctuations. …”
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Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models by Yin Kan Phua, Tsuyohiko Fujigaya, Koichiro Kato

“…Our models predicted the anion conductivity for a diverse set of unseen AEM materials with high accuracy (root mean squared error = 0.014 S cm−1), regardless of their state (freshly synthesized or degraded). …”
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Optimization of multi-objective feature regression models for designing performance assessment methods in college and university educational reform by Fengjun Qi, Zhenping Liu, Wenzheng Zhang, Zhenjie Sun

“…Moreover, its adaptable framework suggests potential applicability across a range of other domains.…”
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An X-Band 6-Bit Hybrid True Time Delay With Linearly-Controlled Gₘ-C All-Pass Filter in 28nm FDSOI CMOS by Soon-Hyeok Kwon, Jung-Dong Park

“…Measurement results validate the circuit’s performance, demonstrating excellent group delay accuracy with an RMS error of less than 1.56 ps across the operating frequency range. …”
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TableBorderNet: A Table Border Extraction Network Considering Topological Regularity by Jing Yang, Shengqiang Zhou, Xialing Li, Yuchun Huang, Honglin Jiang

“…Experiments demonstrate that the method achieves an Intersection-over-Union of 94.2% and a topological error of 1.07%, outperforming existing approaches. …”
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Deep Learning Inter-city Roads Conditions in East Africa for Infrastructure Prioritization using Satellite Imagery and Mobile Data by Davy Uwizera, Prof. Patrick McSharry, Charles Ruranga

“…These surveys are time-consuming, expensive, and vulnerable to human error. Road building and maintenance, on the other hand, has long experienced corruption due to a lack of accountability and validation of conventional approaches to determining which areas to prioritize. …”
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The Re-Modeling of a Polymeric Drug Delivery System Using Smart Response Surface Designs: A Sustainable Approach for the Consumption of Fewer Resources by Magdy M. Aly, Shaimaa S. Ibrahim, Rania M. Hathout

“…However, DOD proved to be more predictive as it scored a lower percentage relative error. <b>Conclusion</b>: The demonstrated resilience of both models, despite the introduction of an outlier, further validates their utility in real-world applications, instead of the exhaustive full-factorial design.…”
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Derivation and Experimental Validation of a Parameterized Nonlinear Froude–Krylov Force Model for Heaving-Point-Absorber Wave Energy Converters by Houssein Yassin, Tania Demonte Gonzalez, Gordon Parker, Giorgio Bacelli, Carlos Michelen

“…Incorporating a scattering model further enhanced force prediction, reducing error across the tested conditions. The outcomes of this work contribute to a more comprehensive understanding of FK forces across a broader range of buoy configurations, simplifying the calculation of the excitation force by adopting a parameterized algebraic model and extending this model to accommodate irregular wave conditions.…”
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Distributed Photovoltaic Power Energy Generation Prediction Based on Improved Multi-objective Particle Algorithm by Yuanzheng Xiao, Huawei Hong, Feifei Chen, Xiaorui Qian, Ming Xu, Hanbin Ma

“…Key results from two power stations included a maximum deviation of 6.2 MW at Power Station A, a peak time deviation of less than 0.1 MW at Power Station B, and a prediction interval error controlled below 30 MW at an 80% confidence level. …”
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Lethality level casualty assessment method for earthquake landslide hazards based on the expansion effect of the mortality rate by Xia Chaoxu, Qi Wenhua, Li Huayue, Nie Gaozhong

“…Validation using historical earthquake cases shows that the calculated casualties error within ± 30% of actual recorded values across different regions and magnitudes. …”
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Physics-integrated neural networks for improved mineral volumes and porosity estimation from geophysical well logs by Prasad Pothana, Kegang Ling

“…Compared to traditional artificial neural networks (ANN), the PINN approach demonstrates higher accuracy and better generalizability, significantly enhancing predictive performance on unseen well datasets. The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN, highlighting the superior accuracy of the PINN approach. …”
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Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…Early detection is essential in reducing mortality rates, but traditional diagnostic methods, such as manual examination of cytological and histological images, are time-consuming and prone to human error. To address these limitations, our study presents a comprehensive platform for bio-image processing and early diagnosis of cervical cancer, employing advanced machine learning and deep learning techniques. …”
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Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data by Junhua Wang, Chengmin Li, Ting Fu, Lanfang Zhang, Anae Sobhani, Jiangtian Xue

“…The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. …”
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Pulsatile Physiological Control of Blood Pump-Cardiovascular System Based on Feedforward Compensation by Yanjun Bao, Teng Jing, Weimin Ru, Ling Zhou

“…Simulation shows that the designed feed-forward compensation link reduces the tracking error of the pulsatile physiological control by 80%. …”
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Compound Mask for Divergent Wave Imaging in Medical Ultrasound by Zahraa Alzein, Marco Crocco, Daniele D. Caviglia

“…However, the spatial and contrast resolution crucially depends on the weights applied in the compound phase, whose optimization is often cumbersome and based on trial and error. This study addresses these limitations by introducing a closed-form approach that maps the transmit apodization weights used in synthetic aperture imaging into the compound mask applied to divergent wave imaging. …”
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Improving LUR testing accuracy and practicability for prestressed concrete sleepers through bending stiffness and visco-plastic deflection-based analysis techniques by Ferhat Çeçen, Bekir Aktaş, Hakan Öztürk, İrfan Ş. Öztürk, M. Burhan Navdar

“…Furthermore, this approach significantly reduces the potential for human error and holds promise for future automation. These advancements make the proposed method highly applicable for both quality control and design of prestressed concrete railway sleepers, warranting further validation across different sleeper types and prestressed concrete structures.…”
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GAN-based solar radiation forecast optimization for satellite communication networks by Chao Chen, Xin Liu, Shizhong Zhao, Muhammad Bilal

“…It reduces the Root Mean Square Error by approximately 3.2% and increases the Structural Similarity Index from 0.84 to 0.87 when compared to the baseline ED-AttUNet model. …”
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Deep Learning-Based FSS Spectral Characterization and Cross-Band Migration by Lei Gong, Xuan Liu, Pan Zhou, Liguo Wang, Zhiqiang Yang

“…Using only 23.1% of the data volume, we achieved training results comparable to those obtained with the full dataset in a significantly shorter time frame, with a mean absolute error of 0.07 on the test set. This demonstrates the feasibility of transfer learning and successfully implements cross-band transfer learning of convolutional neural networks from the terahertz band to the millimeter-wave band. …”
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EMVAS: end-to-end multimodal emotion visualization analysis system by Xianxun Zhu, Heyang Feng, Erik Cambria, Yao Huang, Ming Ju, Haochen Yuan, Rui Wang

“…Abstract Accurately interpreting human emotions is crucial for enhancing human–machine interactions in applications such as driver monitoring, adaptive learning, and smart environments. …”
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Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa by Ropo Ebenezer Ogunsakin, Johnson Adedeji Olusola, Kemi Funlayo Akeju, Adigun Kehinde Abimbola

“…The spatial clustering relationships between COVID-19 mortality and the socioeconomic indicators were analysed using ordinary least squares (OLS), spatial lag models (SLM), and spatial error models (SEM). We observed the clustering of countries for COVID-19 mortality, signifying spatial correlation within the countries of Africa. …”
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