Decrease in loss during the training process. by Muhammad Idrees (92773)

Linear regression analysis. by Young Kyu Lee (14076058)

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Youthful systemic milieu in young recipients decreased apoptosis and oxidative stress damage in allografts, and promoted proliferation. by Chengjun Yu (12136227)

The linearly decreasing convergence factor a. by Junhao Wei (6816803)

Linear regressions of consumer stable carbon and nitrogen isotope values and collection date. by Nathan D. McTigue (10706460)

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S2 File - Factors influencing effective decrease of controlled attenuation parameters in metabolic-associated steatotic liver disease: A multilevel linear regression analysis at Vajira Hospital by Sonsawan Sangprasert (22772538)

“…S2 File - <p>Factors influencing effective decrease of controlled attenuation parameters in metabolic-associated steatotic liver disease: A multilevel linear regression analysis at Vajira Hospital</p>…”

Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate decreases, the range of residual variations also becomes smaller. by Hua Huo (1818133)

“…<p>Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate decreases, the range of residual variations also becomes smaller.…”

Flow chart of Average Value-based control system. by Gourab Saha (8987405)

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Association between the sum of health behaviors and decrease in physical health from before to during the pandemic.... by Elie Mulhem (22537863)

“…<p>Association between the sum of health behaviors and decrease in physical health from before to during the pandemic. …”

Linear relationships (Spearman’s r (p-value)) the treatment-induced changes in clinical and neurophysiological data. by Soheila Qanbari (20455173)

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Linear regressions of pPOM and sPOM stable carbon and nitrogen isotope values versus collection date. by Nathan D. McTigue (10706460)

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Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

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Detailed values of predictors and random effects for the linear mixed effects model explaining SSQ_evol scores. by Luca Fantin (18241864)

Comparative analysis of δ¹³C and δ¹⁵N isotopes in camelid bone and fiber using Generalized Linear and Linear Mixed Effects Models. by Sarah J. Noe (19846136)

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Estimated mean values for light interception. by Torsten Schober (20485754)

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Block diagram for Average Value-based irrigation system. by Gourab Saha (8987405)

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The values of angiotensin II by Ningning Wang (560944)

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Linear single-track dynamics model. by Tuan Anh Nguyen (121944)

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The selection of indicators based on their correlation with ground surface data. by Abdolhossein Boali (19996390)

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Obstacle uniform linear motion scenarios. by Long Di (9977453)

“…Furthermore, a probabilistic obstacle motion prediction framework is established through motion pattern analysis to actively optimize the robot’s motion strategy and reduce tracking errors. Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”