Journal: Scientific Reports

Article Title: An inclusive classification optimization model for land use and land cover classification

doi: 10.1038/s41598-025-91260-0

Figure Lengend Snippet: Classification results in a typical urban area (104.09014 °E, 30.61249 °N), ( a ) is the composite Landsat image from 2020 for classification; ( b ) is the historical image from World Imagery Wayback, 12/16/2020; ( c ) is the classification result from ESA’s WC; ( d ) is the result from Google’s DW; ( e ), ESRI’s LULCM; ( f ), CAS’s GLC-FCS30; ( g ), WHU’s CLCD; ( h ) is the classification result of gradient tree boosting (GTB) from GEE, the best one of the six classifiers’ results.

Article Snippet: Fig. 7 Classification results in a typical urban area (104.09014 °E, 30.61249 °N), ( a ) is the composite Landsat image from 2020 for classification; ( b ) is the historical image from World Imagery Wayback, 12/16/2020; ( c ) is the classification result from ESA’s WC; ( d ) is the result from Google’s DW; ( e ), ESRI’s LULCM; ( f ), CAS’s GLC-FCS30; ( g ), WHU’s CLCD; ( h ) is the classification result of gradient tree boosting (GTB) from GEE, the best one of the six classifiers’ results.

Techniques:

Journal: Scientific Reports

Article Title: An inclusive classification optimization model for land use and land cover classification

doi: 10.1038/s41598-025-91260-0

Figure Lengend Snippet: Classification results in a typical suburban area (103.871413 °E, 30.504502 °N), ( a ) is the composite Landsat image of 2020 for classification; ( b ) is the historical image from World Imagery Wayback, 12/16/2020; ( c ) is the classification result from ESA’s WC; ( d ) is the result from Google’s DW; ( e ), ESRI’s LULCM; ( f ), CAS’s GLC-FCS30; ( g ), WHU’s CLCD; ( h ) is the classification result of the minimum distance (MD) from GEE, the best one of the six classifiers’ results.

Article Snippet: Fig. 7 Classification results in a typical urban area (104.09014 °E, 30.61249 °N), ( a ) is the composite Landsat image from 2020 for classification; ( b ) is the historical image from World Imagery Wayback, 12/16/2020; ( c ) is the classification result from ESA’s WC; ( d ) is the result from Google’s DW; ( e ), ESRI’s LULCM; ( f ), CAS’s GLC-FCS30; ( g ), WHU’s CLCD; ( h ) is the classification result of gradient tree boosting (GTB) from GEE, the best one of the six classifiers’ results.

Techniques:

Journal: Global Spine Journal

Article Title: Risk Factors for Failure of Non-operative Management in Isolated Unilateral Non-displaced Facet Fractures of the Subaxial Cervical Spine: Systematic Review and Meta-Analysis

doi: 10.1177/21925682241304351

Figure Lengend Snippet: Characteristics of Included Studies.

Article Snippet: Despite using different facet injury classifications (A3 from Dvorak, and F3 from AOSpine ), both papers compared floating lateral mass fractures with less complex fractures.

Techniques: Biomarker Discovery, Isolation

Journal: Pathogens and Disease

Article Title: pic gene of enteroaggregative Escherichia coli and its association with diarrhea in Peruvian children

doi: 10.1093/femspd/ftw054

Figure Lengend Snippet: CART model in EAEC strains from diarrhea and control samples. For the analysis, the software Salford Predictive Modeler 8.0 considering the 18 virulence genes analyzed was used. Each node is related with the presence (1) or absence (0) of each gene (red boxes are terminal nodes). Inside each box the distribution of the gene among diarrhea (blue bars) and control samples (red bars) is shown.

Article Snippet: The classification and regression tree (CART) software (Salford Predictive Modeler v8.0; Salford Systems) inputing all genes of interest as binary (present/absent) independent predictive variables was used.

Techniques: Control, Software