On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…We compared different types of covariates (meteorological without precipitation, precipitation, remote sensing, and plant functional types) and their impact on model performance at the site level in a cross-validation setup.…”
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Distinctive Gait Variations and Neuroimaging Correlates in Alzheimer's Disease and Cerebral Small Vessel Disease by Xia Zhou, Wen‐Wen Yin, Chao‐Juan Huang, Si‐Lu Sun, Zhi‐Wei Li, Ming‐Xu Li, Meng‐Meng Ren, Ya‐Ting Tang, Jia‐Bin Yin, Wen‐Hui Zheng, Chao Zhang, Yu Song, Ke Wan, Yue Sun, Xiao‐Qun Zhu, Zhong‐Wu Sun

“…Gait metrics included the timed up and go (TUG) test, dual‐task TUG (DTUG) test, Berg balance scale (BBS), dual‐task cost (DTC), step length, gait speed, cadence and coefficient of variation of gait. The relationships among structural and perfusion variations, gait metrics and cognitive function were examined. …”
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Steady-State Dynamical Response of a Strongly Nonlinear System with Impact and Coulomb Friction Subjected to Gaussian White Noise Excitation by Guidong Yang, Dongmei Huang, Wei Li, Meng Su

“…The Zhuravlev nonsmooth transformation of the state variables combined with the Dirac delta function is utilized to simplify the original system to one without velocity jump. …”
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Biotinylated fusion protein-streptavidin-peroxidase complex based non-competitive magnetic immunoassay for rapid detection of organophosphorus pesticides by Ruirui Shi, Jiangke Li, Wenli Yu, Tengfei Yin, Haiyan Lin, Juan Liu, Wenyu Cui

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Modeling Overall Survival in Patients With Pancreatic Cancer From a Pooled Analysis of Phase II Trials by Eva Rahman Kabir, Faruque Azam, Tanisha Tabassum Sayka Khan, Hasina Yasmin, Namara Mariam Chowdhury, Syeda Maliha Ahmed, Baejid Hossain Sagar, Nasrin Ahmed Tahrim

“…The relationship between predictors and OS was explored by a gamma generalized linear model (GLM) with a log‐link function and compared with linear models. Results The Spearman rank correlation coefficient between PFS/TTP and OS was 0.88 (95% confidence interval [CI] 0.85–0.89; p < 0.0001; n = 610) and between ORR and OS was 0.58 (0.52–0.64; p < 0.0001; n = 514). …”
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Sensitivity analysis of melting heat transfer in Williamson fluid by Aliya Batool, Zakir Hussain, Dilawar Hussain

“…The model is simplified to ordinary differential equations by introducing similarity variables and tackled by bvp4c Matlab function. The significant variables on flow model are identified by Response Surface Method (RSM). …”
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qDESH: a method to quantify disproportionately enlarged subarachnoid space hydrocephalus by Sofia Behndig, Afroditi Lalou, Jan Axelsson, Jenny Larsson, Anders Wåhlin, Pavel Ryska, Ondrej Slezak, Katrine Riklund, Jan Zizka, Jan Malm, Anders Eklund

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Radar-equivalent snowpack: reducing the number of snow layers while retaining their microwave properties and bulk snow mass by J. Meloche, N. R. Leroux, B. Montpetit, V. Vionnet, C. Derksen

“…A reduction in the mathematical complexity of SWE retrieval cost functions and a reduction in computation of up to 80 % can be gained by using fewer layers in the SWE retrieval.…”
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Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow by Amin Gharehbaghi, Redvan Ghasemlounia, Farshad Ahmadi, Rasoul Mirabbasi, Ali Torabi Haghighi

“…The Pearson’s correlation coefficient (PCC) and Cosine Amplitude Sensitivity (CAS) as feature (input) selection process determine the only precipitation (P m ) as the most effective input variable among a list of on-site potential climate time series parameters recorded in the study area. …”
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An assessment of the likely impact of strain-related phenotypic plasticity on hominin fossil species identification by M. Collard, S.J. Lycett

“…We divided the measurements into three groups: measurements of features subject to high masticatory strain, measurements of features subject to low-to-moderate masticatory strain, and measurements of features that do not remodel and therefore are not prone to strain-related phenotypic plasticity. Next, we used the coefficient of variation and ANOVA to investigate whether masticatory strain is a cause of variability. …”
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Mobile machine design through dynamic load simulation on their drive units by S. A. Partko, L. M. Groshev, A. N. Sirotenko

“…The root cause for the occurrence of vibration effects is the profile irregularity of the mobile machine path, and the variability of physicomechanical characteristics of the soil. …”
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Intelligent Field Sensor Station for Monitoring Agrophysical Parameters and Phenotyping in Precision Agriculture System by S. A. Vasilyev, S. Ye. Limonov, S. A. Mishin

“…(Results and discussion) The intelligent field sensor station successfully demonstrated its efficiency, confirming both its functionality and reliability in simultaneous data collection. …”
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Observational partitioning of water and CO₂ fluxes at National Ecological Observatory Network (NEON) sites: a 5-year dataset of soil and plant components for spatial... by E. Zahn, E. Bou-Zeid

“…<p>Long-term time series of transpiration, evaporation, plant net photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. …”
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Artificial intelligence Defect Detection Robustness inReal-time Non-Destructive Testing of Metal Surfaces by Chaoyu Dong, Jovian Sanjaya Putra, Andrew A. Malcolm

“…Their structural integrity, durability, and functionality depend heavily on their quality, making surface inspection a vital process. …”
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Genome-wide association study of blood vitamin D metabolites and bone remodelling markers in pigs by Dipanwita Paul, Michael Oster, Siriluck Ponsuksili, Klaus Wimmers, Henry Reyer

“…Hence, mineral utilization efficiency might be indirectly improved which remains to be empirically demonstrated through further research.…”
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Optimizing Load Frequency Control of Multi-Area Power Renewable and Thermal Systems Using Advanced Proportional–Integral–Derivative Controllers and Catch Fish Algorithm by Saleh A. Alnefaie, Abdulaziz Alkuhayli, Abdullah M. Al-Shaalan

“…Additionally, by contrasting the performance of the PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers, the efficiency of the CFA is confirmed. Additionally, a sensitivity analysis that considers simultaneous modifications of the frequency bias coefficient (B) and speed regulation (R) within a range of ±25% validates the efficacy and dependability of the suggested CFA-tuned PI(1+DD). …”
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Speciation and mobilization of ultra-trace Hg(II) in groundwater by Kunfu PI, Yanxin WANG, Juewen LIU, Ya’nan YANG, Van Cappellen PHILIPPE

“…Applying tests to hydrogeochemically diverse groundwaters from the Grand River Watershed, Canada, the results indicate that the DNA-functionalized hydrogel is able to quickly detect dissolved Hg(II) but inapplicable to low Hg(II) concentrations (<1.60 μg/L), whereas the DNA-DGT sensor can capture variably ultra-trace Hg(II) species depending on the deployment time. …”
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Testing the Level of Alternative Institutions as a Slowdown Factor of Economic Development: the Case of Montenegro by Mimo Draškovic, Sanja Bauk, Dalia Streimikiene, Veselin Draskovic

“…On the basis of the conducted statistical examines: standard error of the regression estimate, correlation coefficient, and coefficient of determination are calculated on the basis of previously determined regression coefficients and forecast values of the linear function of free variables (factors: a, b, and c). …”
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Bifurcations of Spatially Inhomogeneous Solutions of a Boundary Value Problem for the Generalized Kuramoto–Syvashinsky Equation by Alina V. Sekatskaya

“…In this paper, a differential partial equation with an unknown function of three variables time and two spatial variables – is considered. …”
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Research and development of thick plate shape prediction system based on industrial big data by Yufei MA, Changxin LIU, Wei KONG, Jinliang DING

“…Thick plate shape is one of the important indicators to measure the quality of thick plate products.The timely prediction of the final plate shape in production is of great significance for adjusting the operation and control of thick plate production.In actual industrial production, thick plate data has many characteristics, such as multiple coupling information, large amount of redundant information, and multi-source heterogeneity of data.Combining the needs of thick plate shape prediction, a thick plate shape prediction system was designed and developed.The data dump function was used to filter and preprocess the industrial big data to remove the coupling information and redundant variables in the data.LSTM neural network, convolutional neural network and 3D convolutional neural network were used to extract data features from data of different dimensions, and the features were fused based on the maximum mutual information coefficient to establish an integrated learning prediction model, which effectively solved the modeling difficulties caused by multi-source heterogeneous data.The actual industrial data of a domestic thick plate production line was used for verification, and the results showed the effectiveness of the developed system.…”
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