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

STL Linear Combination Forecast Graph. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

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

“…<div><p>Ensuring that a robot employing demonstration learning models can simultaneously achieve accurate trajectory tracking of demonstrated paths and effective avoidance of moving obstacles in dynamic environments remains a critical research challenge. …”

Heteroscedastic linear mixed model main results, estimating the volume (ml) of beer sold per day on the transformed (cube root) scale, on the original scale and on the relative sca... by Eleni Mantzari (6991172)

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Model and learning rule. by Janis Keck (21587252)

“…The right column has the same pre- and postsynaptic activities as the left column, only in reverse order. In <b>(C)</b>, the learning rule with parameters is used, while in <b>(D)</b> Only in the latter the synaptic weight changes are preserved (in reverse order), while in <b>(C)</b>, postsynaptic activity before presynaptic activity leads to a net weight decrease. …”

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

The maps illustrating wind erosion in northeastern Iran for 2008 and 2023, generated using various models from the SDM package. by Abdolhossein Boali (19996390)

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Fig 8 - by Abdolhossein Boali (19996390)

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Fig 5 - by Abdolhossein Boali (19996390)

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Importance of variables in the study area in 2023, generated in the R 4.3.2 software (www.r-project.org) and Microsoft Office Excel 2023 (https://www.microsof.com). by Abdolhossein Boali (19996390)

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Fig 1 - by Abdolhossein Boali (19996390)

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Wind erosion monitoring during the 15 years (2008–2023) using the ensemble model, generated in the ArcGIS 10.7.1 software (www.esri.com) and R 4.3.2 software (www.r-project.org). by Abdolhossein Boali (19996390)

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Methodological flowchart for this study, generated in Microsoft Office Word 2023 (https://www.microsof.com). by Abdolhossein Boali (19996390)

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Fig 4 - by Abdolhossein Boali (19996390)

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Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model establishment process. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Structure diagram of GBDT model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Estimation of the coefficients of a generalised linear model of posttraumatic stress symptomatology. by Beatriz Arregui-Gallego (21553735)

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”