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  1. 2121

    Numerical discussion of phase-lag mixed integral equations with general discontinuous kernels by Abeer M. Al-Bugami, M. A. Abdou

    Published 2025-05-01
    “…This integral equation has coefficients that vary in time and phase-lag time. It was proven that this method linked the time variables to the kernel of the integral equation in terms of the existence of a single solution. …”
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  2. 2122
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  4. 2124

    EVALUATION OF A DECRESE IN WORK PRODUCTIVITY IN PATIENTS WITH RHEUMATOID ARTHRITIS by Olga Yuryevna Vakulenko, D V Goryachev, O A Krichevskaya, Sh F Erdes

    Published 2014-02-01
    “…The Spearman correlation coefficients with WPAI indicators were higher than 0.4 in all the cases. …”
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  5. 2125

    Application of Post-Occupancy Evaluation in the Design of Collegiatae Educational Space: (Case study: Faculty of Arts and Architecture, Islamic Azad University-Tehran South Branch) by Reza Naghdbishi, Zeinab Gohari

    Published 2024-02-01
    “…This analysis identified the relationship between research variables and evaluated their impact on the design. …”
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  6. 2126

    Assessment of upper limb motor control: establishing normative benchmarks for clinical applications by Pablo Martín-Sierra, Cristina Sanchez, Eloy Urendes, Rafael Raya

    Published 2025-08-01
    “…A combination of clinical functional scales with the quantitative metrics presented here is expected to enable better evaluation of motor control. …”
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  7. 2127

    Hierarchical GraphCut Phase Unwrapping Based on Invariance of Diffeomorphisms Framework by Xiang Gao, Xinmu Wang, Zhou Zhao, Junqi Huang, Xianfeng David Gu

    Published 2025-01-01
    “…The resulting label maps are fused via majority voting to efficiently and robustly estimate the unwrapped phase count <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> at each pixel, using an odd number of votes to break ties. …”
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  8. 2128

    Parametric-modeling-based multi-objective thermoelastic optimization of rudder structures by Shi Guanghui, Bao Yuhao, Wu Wenhua, Guo Guiqiang, Lin Ye, Zhang Xiaopeng, Tao Ran

    Published 2025-01-01
    “…To efficiently analyze and optimize the structural performance of rudder structures, a parametric optimization model for the radial configuration of reinforcement ribs is constructed. …”
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  9. 2129

    AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images by Ruifen Cao, Qingbin Meng, Dayu Tan, Pijing Wei, Yun Ding, Chunhou Zheng

    Published 2024-11-01
    “…Moreover, the coarse predictions and refined predictions are combined by using a loss function that employs cross-entropy loss and generalized dice loss to efficiently tackle the challenge of class imbalance among nuclei in histology images. …”
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  10. 2130

    Drying kinetics and thermodynamic analysis; enhancing quinoa (Chenopodium quinoa Willd.) quality profile via pre-treatments assisted germination and processing by Jabir Khan, Palwasha Gul, Qingyun Li, Kunlun Liu

    Published 2025-06-01
    “…Principal Component Analysis revealed significant correlations between analyses, explaining 80.37 % variability. The intensity of functional groups decreased in the infrared spectra, although the transmission of signals was greater in pretreated samples than in control. …”
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  11. 2131

    Emerging Healthcare Technologies in Gastrointestinal Endoscopy: 75 Years of Evolution by Irina Florina CHERCIU HARBIYELI, Elena Daniela BURTEA, Vlad IOVANESCU, Dan Nicolae FLORESCU, Ion ROGOVEANU, Dan Ionut GHEONEA, Adrian SAFTOIU

    Published 2025-05-01
    “…Future capsules will feature AI-driven lesion detection and therapeutic functions, higher-resolution imaging, wider fields of view, controllable movement bringing the concept of "swallowing the gastroenterologist" closer to reality. …”
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  12. 2132

    Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model by Moses Oluwatobi Fajobi, Olumuyiwa Ajani Lasode, Adekunle Akanni Adeleke, Peter Pelumi Ikubanni, Ayokunle Olubusayo Balogun, Prabhu Paramasivam

    Published 2023-01-01
    “…The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. …”
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  13. 2133

    Path model explaining the association between fear of falling and health-related quality of life in (pre-)frail older adults by Tjard Sattler, Sophie Gottschalk, Hans-Helmut König, Tobias Braun, Gisela Büchele, Michael Denkinger, Tim Fleiner, Corinna Nerz, Kilian Rapp, Martina Schäufele, Christian Werner, Judith Dams

    Published 2025-02-01
    “…Unlike previous studies that examined only few mediating variables, this study aimed to develop a more comprehensive path model explaining the association between FoF and HrQoL. …”
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  14. 2134

    Meristic and morphometric comparison of an undescribed sucker of the Río Culiacán (Catostomus sp.) and Yaqui sucker (Catostomus bernardini) (Catostomidae, Teleostei) from the Sierr... by Gorgonio Ruiz-Campos, Sergio Sánchez-Gonzáles, Richard L. Mayden, Alejandro Varela-Romero

    Published 2016-06-01
    “…Likewise, the discrimination was associated with the lowest values for predorsal distance, soft posterior ocular margin to occiput, and number of anal rays. The standardized coefficients for canonical variables 1 and 2 accounted 82.6% of the total variation. …”
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  15. 2135

    Estimativa das temperaturas máximas e mínimas do ar para a região do Circuito das Frutas, SP Estimation of maximum and minimum air temperatures for the "Circuito das Frutas" region... by Ludmila Bardin, Mário J. Pedro Júnior, Jener F. L. de Moraes

    Published 2010-01-01
    “…Also, maps with the spacial variability of the maximum and minimum mean monthly and annual temperatures are presented for the region.…”
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  16. 2136

    Exploring restoration efforts from a social lens: statistical models reveal relationships between salmon habitat restoration efforts and ecological and social characteristics of th... by Brittany D King, Robert Fonner

    Published 2024-12-01
    “…We specified statistical models to explain the variation in the number of restoration worksites undertaken in subwatersheds as a function of environmental and social variables. Using a common set of explanatory variables, we fit four models to examine the distribution of worksites associated with particular types of restoration actions (instream, riparian, land acquisition, and fish passage) and a fifth model to examine the distribution of all aquatic-based restoration worksites across action types. …”
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  17. 2137

    Concurrent Validity of Wearable Nanocomposite Strain Sensor With Two-Dimensional Goniometer and Its Reliability for Monitoring Knee Active Range of Motion in Multiple Participants by Han-Byeol Choi, Yongjun Kim, Ji Young Lim, Seonghee Kim, Sang Yul Yang, Kihyeon Kim, Hyouk Ryeol Choi, Seunghyun Baik, Ji Hye Hwang

    Published 2024-01-01
    “…The range of motion (ROM) of joints in the human body is essential for movement and functional performance. Real-time monitoring of joint angles is crucial for confirming pathologic biomechanics, providing feedback during rehabilitation, and evaluating the treatment efficacy. …”
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  18. 2138

    Influence of Migratory Strategy, Group Size, and Environmental Conditions on the Movements of Caribou in Eastern Alaska by Kyle Joly

    Published 2025-05-01
    “…The ability of caribou (<i>Rangifer tarandus granti</i>) to efficiently move long distances and have a high degree of plasticity in their movements allows them to respond and be resilient to dynamic and dramatic differences in environmental conditions. …”
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  19. 2139

    Development and validation of a novel prediction model for osteoporosis: from serotonin to fat-soluble vitamins by Jinpeng Wang, Lianfeng Shan, Jing Hang, Hongyang Li, Yan Meng, Wenhai Cao, Chunjian Gu, Jinna Dai, Lin Tao

    Published 2025-02-01
    “…Stepwise discriminant analysis was performed to identify efficient predictors for osteoporosis. The prediction model was developed based on Bayes and Fisher’s discriminant functions, and validated via leave-one-out cross-validation. …”
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  20. 2140

    Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys by Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy

    Published 2025-04-01
    “…This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. Among the ML methods explored, a backpropagation neural network (BPNN) model with a sigmoid activation function exhibited superior predictive accuracy compared to other algorithms. …”
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