Spatial distribution of the grand canal (East Zhejiang section)-maritime silk road cultural heritage. by Jie Li (15030)

Various natural factors and spatial distribution of the Grand Canal (East Zhejiang section)-Maritime Silk Road cultural heritage. by Jie Li (15030)

Cultural heritage dataset. by Jie Li (15030)

“…The Three-River Estuary is the high-density core area, with the number and density of cultural heritage decreasing as its distance increases. (3) Distribution characteristics of cultural heritage vary across different periods. …”

Image 2_Spatial deciphering of the transcriptomic heterogeneity of tumor spread through air spaces in lung cancer.tif by Wenhao Wang (515852)

“…Background<p>Spread through air spaces (STAS) represents a novel invasion mechanism in adenocarcinoma that considerably influences lung cancer clinical outcomes; however, studies of its mechanisms at the spatial level are lacking.</p>Methods<p>We used the NanoString GeoMx digital spatial profiling (DSP) technology to conduct a spatial transcriptomic analysis of surgically resected tissues from non-small-cell lung cancer (NSCLC) patients with or without STAS.…”

Image 4_Spatial deciphering of the transcriptomic heterogeneity of tumor spread through air spaces in lung cancer.tif by Wenhao Wang (515852)

“…Background<p>Spread through air spaces (STAS) represents a novel invasion mechanism in adenocarcinoma that considerably influences lung cancer clinical outcomes; however, studies of its mechanisms at the spatial level are lacking.</p>Methods<p>We used the NanoString GeoMx digital spatial profiling (DSP) technology to conduct a spatial transcriptomic analysis of surgically resected tissues from non-small-cell lung cancer (NSCLC) patients with or without STAS.…”

Data Sheet 1_Spatial deciphering of the transcriptomic heterogeneity of tumor spread through air spaces in lung cancer.zip by Wenhao Wang (515852)

“…Background<p>Spread through air spaces (STAS) represents a novel invasion mechanism in adenocarcinoma that considerably influences lung cancer clinical outcomes; however, studies of its mechanisms at the spatial level are lacking.</p>Methods<p>We used the NanoString GeoMx digital spatial profiling (DSP) technology to conduct a spatial transcriptomic analysis of surgically resected tissues from non-small-cell lung cancer (NSCLC) patients with or without STAS.…”

Image 1_Spatial deciphering of the transcriptomic heterogeneity of tumor spread through air spaces in lung cancer.tif by Wenhao Wang (515852)

“…Background<p>Spread through air spaces (STAS) represents a novel invasion mechanism in adenocarcinoma that considerably influences lung cancer clinical outcomes; however, studies of its mechanisms at the spatial level are lacking.</p>Methods<p>We used the NanoString GeoMx digital spatial profiling (DSP) technology to conduct a spatial transcriptomic analysis of surgically resected tissues from non-small-cell lung cancer (NSCLC) patients with or without STAS.…”

Image 3_Spatial deciphering of the transcriptomic heterogeneity of tumor spread through air spaces in lung cancer.tif by Wenhao Wang (515852)

“…Background<p>Spread through air spaces (STAS) represents a novel invasion mechanism in adenocarcinoma that considerably influences lung cancer clinical outcomes; however, studies of its mechanisms at the spatial level are lacking.</p>Methods<p>We used the NanoString GeoMx digital spatial profiling (DSP) technology to conduct a spatial transcriptomic analysis of surgically resected tissues from non-small-cell lung cancer (NSCLC) patients with or without STAS.…”

SEAwise report on effects of spatial management measures suggested in SEAwise to safeguard species, habitats and choke species on fisheries selectivity and fuel cost by Francois Bastardie (6217991)

“…Combining the closed area with the PGY scenario, a catch reduction of juveniles also occurred. The spatial <b>BEMTOOL</b> component showed that the closures would increase fuel consumption of the >18 m fleets in South GSA 17 by 12% but decreased the fuel consumption of the >18m fleets in the center of GSA 19 by 18%. …”

SEAwise synthetic summary report of the findings of WP4 on changes to the ecosystem impacts of fishing in response to spatial management for online tool by Anna Rindorf (6217391)

Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf by Sanglin Zhao (20915732)

“…The results show that: (1) From 2000 to 2035, China’s total carbon emissions increased year by year, but the growth rate of carbon emissions gradually decreased; The carbon emission structure is “secondary industry > residents’ life > tertiary industry > primary industry”, and the growth rate of carbon in secondary industry and residents’ life is faster, while the change trend of primary industry and tertiary industry is smaller; (2) The spatial distribution of carbon emissions in China’s provinces presents a typical pattern of “eastern > central > western” and “northern > southern”, and the carbon emission centers tend to move to the northwest; (3) The regions with high level of digital economy, advanced industrial structure and new quality productivity have relatively less carbon emissions, which has significant group difference effect; (4) The intensity effect of energy consumption is the main factor driving the continuous growth of carbon emissions, while the per capita GDP and the structure effect of energy consumption are the main factors restraining carbon emissions, while the effects of industrial structure and population size are relatively small. …”

Algorithm training accuracy experiments. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

Repeat the detection experiment. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

Detection network structure with IRAU [34]. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

Ablation experiments of various block. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

Kappa coefficients for different algorithms. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

The structure of ASPP+ block. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

The structure of attention gate block [31]. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

DSC block and its application network structure. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”

The structure of multi-scale residual block [30]. by Yingying Liu (360782)

“…However, after removing the integrated residual attention unit and depth-wise separable convolution, the accuracy decreased by 1.91% and the latency increased by 117ms. …”