Image 6_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 10_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 3_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 5_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 9_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 4_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 11_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 6_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 2_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Design of stiffened panels for stress and buckling via topology optimization: data by Sheng Chu (19655605)

“…To solve the optimization problem, a semi-analytical sensitivity analysis is performed, and the optimization algorithm is outlined. Numerical investigations demonstrate and validate the proposed method.…”

Echo Peak by Rocco De Marco (14146593)

“…</p><p dir="ltr">For classification, the algorithm iteratively processes the audio in overlapping time windows. …”

Structure of optimized model parameters in the high-dimensional cases. by Kevin J. Wischnewski (21354521)

“…The number and size of the clusters were determined with help of the -means clustering method. Both were set to zero if the absolute mean value of the off-diagonal elements in the correlation matrix (cf. …”

In this paper, we use the term AI in its broadest sense. by Aidan Crilly (21743791)

“…<p>It thus covers a wide-ranging set of algorithms, including machine-learning and deep-learning techniques as subcategories, as illustrated here. …”

Identify different types of urban renewal implementations at streetscape scale by Xiaotong Wang (20852492)

“…Existing research primarily focuses on detecting pixel-level or object-level changes in urban physical space, often neglecting the semantic complexity inherent in urban renewal. …”

Identification of ferroptosis-related LncRNAs as potential targets for improving immunotherapy in glioblastoma by Zhaochen Wang (12176245)

“…<p>The effect of ferroptosis-related long non-coding RNAs (lncRNAs) in predicting immunotherapy response to glioblastoma (GBM) remains obscure. …”

Supplementary file 1_An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction.docx by Songpeng Zhao (21563714)

“…Three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (Gradient Boosting)-were integrated into a multi-level stacking ensemble, with Support Vector Regression serving as the meta-learner. …”

Supplementary Material for: Investigating the value of umbilical artery eucapnic pH as a biomarker for adverse neonatal outcomes: A systematic review by figshare admin karger (2628495)

“…However, conventional potential of hydrogen (pH) and base deficit (BD) methods have limitations in neonates. …”

Turkish_native_goat_genotypes by Yalçın YAMAN (20209833)

“…</p><p dir="ltr">Nine heterogeneous machine-learning models—including penalised linear methods, gradient-boosting algorithms, and interaction-sensitive tree ensembles—were applied to examine genotype–phenotype relationships. …”

Data Sheet 1_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…Background<p>Water and nitrogen are essential elements prone to deficiency during plant growth. Current water–fertilizer monitoring technologies are unable to meet the demands of large-scale Glycyrrhiza uralensis cultivation. …”

Data Sheet 2_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…Background<p>Water and nitrogen are essential elements prone to deficiency during plant growth. Current water–fertilizer monitoring technologies are unable to meet the demands of large-scale Glycyrrhiza uralensis cultivation. …”