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. …”

OpenCitations Meta RDF dataset of all bibliographic metadata and its provenance information by OpenCitations ​ (3068259)

“…The JSON-LD files inside the archives are further compressed using the zip algorithm. It is recommended to process these inner files as compressed without extracting them, to manage data more efficiently.…”

Quasar Feedback Survey 2: C-band maps. by Ann Njeri (14606546)

“…</p><p dir="ltr">We used the VLA Calibration Pipeline 2024.1.0.8 for casa version 6.6.1 to reduce our data. Imaging was performed using the casa tclean algorithm with Briggs weighting of r=0.5 for all radio maps.…”

Oryza australiensis species-specific gene and protein candidates by Sabrina Morrison (19846869)

“…<i>japonica </i>Illumina read data was extracted from the NCBI (SRR15967546 & SRR15967547) and mapped against the <i>O. australiensis </i>gene list to identify candidate unique coding sequences. …”

Tjøtta lambing dataset by Pedro Gonçalves (13118880)

“…This dataset has the potential to train an algorithm for classifying birth-related behaviors, paving the way for an automated lambing detection system.…”

On a Deterministic Framework for Highly Compressed Reversible Representations of Symbolic Genomic Entities by Tahir Bhatti (20961974)

“…No detailed implementation, algorithms, or source code are disclosed in this concept version. …”

Quasar Feedback Survey 2: L-band maps by Christopher Harrison (9448751)

“…</p><p dir="ltr">We used the VLA Calibration Pipeline 2024.1.0.8 for casa version 6.6.1 to reduce our data. Imaging was performed using the casa tclean algorithm with Briggs weighting of r=0.5 for all radio maps.…”

Illustration of the BSBL-TSK-FS model architecture. by Leyao Wang (837838)

“…The model uses fuzzy system to process the data, then gets fuzzy feature , and applies to the next module. …”

Equitable Hospital Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning by Emeka Abakasanga (14235503)

“…To address fairness concerns, two bias mitigation techniques were applied: a post-processing threshold optimiser and an in-processing reductions method using an exponentiated gradient. …”

Optimizing a machine-learning model for color design of metal oxides/metal multilayers with physics-guided kernel trick by Dongik Lee (20454362)

“…<p>This study builds and evaluates ML-based prediction models to design the color of metal thin films through oxidation processes. Using images of copper oxides/copper multilayered structures prepared through oxidation of single-crystalline Cu thin films, we apply various regression algorithms, including polynomial regression, random forest regression, and support vector regression (SVR). …”

Eye-tracking during natural reading of stories in Spanish by NeuroLIAA (20641466)

“…This comprehensive resource offers valuable opportunities to investigate eye movement patterns during Spanish reading, explore language-specific cognitive processes, examine Spanish linguistic phenomena, and develop computational algorithms for reading research and natural language processing applications.…”

Robust Bayesian Modeling of Counts with Zero Inflation and Outliers: Theoretical Robustness and Efficient Computation by Yasuyuki Hamura (13729786)

“…<p>Count data with zero inflation and large outliers are ubiquitous in many scientific applications. …”

Table 1_Advances in the application of human-machine collaboration in healthcare: insights from China.docx by Wuzhen Wang (20675405)

“…“Human–machine collaboration” is based on an intelligent algorithmic system that utilizes the complementary strengths of humans and machines for data exchange, task allocation, decision making and collaborative work to provide more decision support. …”