Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios.

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Landslide Susceptibility Mapping by Fusing Convolutional Neural Networks and Vision Transformer.

Landslide susceptibility mapping (LSM) is an important decision basis for regional landslide hazard risk management, territorial spatial planning and landslide decision making. The current convolutional neural network (CNN)-based landslide susceptibility mapping models do not adequately take into account the spatial nature of texture features, and vision transformer (ViT)-based LSM models have high requirements for the amount of training data. In this study, we overcome the shortcomings of CNN and ViT by fusing these two deep learning models (bottleneck transformer network (BoTNet) and convolutional vision transformer network (ConViT)), and the fused model was used to predict the probability of landslide occurrence. First, we integrated historical landslide data and landslide evaluation factors and analysed whether there was covariance in the landslide evaluation factors. Then, the testing accuracy and generalisation ability of the CNN, ViT, BoTNet and ConViT models were compared and analysed. Finally, four landslide susceptibility mapping models were used to predict the probability of landslide occurrence in Pingwu County, Sichuan Province, China. Among them, BoTNet and ConViT had the highest accuracy, both at 87.78%, an improvement of 1.11% compared to a single model, while ConViT had the highest F1-socre at 87.64%, an improvement of 1.28% compared to a single model. The results indicate that the fusion model of CNN and ViT has better LSM performance than the single model. Meanwhile, the evaluation results of this study can be used as one of the basic tools for landslide hazard risk quantification and disaster prevention in Pingwu County.

[Cancer multiplicities in the Czech population]. / Nádorové multiplicity u ceské populace.

During 1976-2005 the Czech Cancer Registry registered 1.486,984 neoplasms of categories ICD-10: C00-D48, of which were notified 290,312 (19.5%) multiple cases. There were 65,292 primary diseases in men and 59,970 in women, 89,796 subsequent neoplasms in men and 75,254 in women. The duplicities were higher in men and multiplicities higher in women. The multiple cases there were 19.5% of total new diagnosed neoplasms in men and 18.5% in women, without the skin cancers there were 13.7% in men and 13.6% in women. The most frequent were primary cancers of skin 46%, digestive tract 13.5%, urinary tract 9.6%, genital organs 8.1%, respiratory and intrathoracic organs 7.8% in men and cancers of skin 39.4%, breast 17.3%, genital organs 14.6%, and digestive tract 9.8% in women. The highest percentage of multiplicities in new diagnosed cases were cancers of skin 56.4%, melanoma 22.7%, urinary tract 19.9%, oral cavity 17.8%, genital organs 16.4%, endocrine glands 15.8% and haemopoietic tissues 13.9% in men, the cancers of skin 43.6%, oral cavity 19.8%, melanoma 18.6%, breast 17.7%, urinary tract 17.7%, genital organs 13.5% and endocrine glands 13% in women. The most multiplicities were registered in region Northern Moravia 38,547 (13.3%), Southern Moravia 34,219 (11.8%) and in Prague 34,218 (11.8%). From 125,262 patients, 18,887 (15.1%) men and 22,274 (17.8%) women survived, number of deceased persons was 46,405 (37%) men and 37,696 (30.1%) women. In the view of multiple cancers the broad education of lifestyle appears not to be sufficient for both the healthy population and for patients with 595 thousand cancers, expected in 2010.