A contrast study of the oesophagus with water-soluble iso-oncotic contrast media as the sole diagnostic imaging modality is safe (avoiding the risk of aspiration pneumonia), reliable (identifying all injuries) and cost-efficient (avoiding the need for additional expensive investigations) in cases of penetrating cervical trauma.
Soil carbon storage results from interactions between ecological processes and contributes to the global chemical regulation of the atmosphere, a vital ecosystem service. Within the ecosystem services approach, measuring soil carbon stock is used as an indicator of landscapes that function as terrestrial carbon sinks and sources. Soil carbon stock models of agricultural landscapes use national carbon stock data and are used to determine environmental benchmarks and develop land-use management strategies for improved landscape-scale carbon sequestration. The InVEST Carbon Storage model has been used as a tool to map carbon stock based on these data. However, the accuracy of the national carbon inventories of Hungary is unknown. In this study, the InVEST soil carbon stock models of two agricultural landscapes in Hungary were produced based on national soil carbon stock data and in-field collected soil sample carbon stock data. Carbon stock inventories were collated and used as InVEST carbon model inputs, and the models were mapped, compared, and evaluated to determine their usefulness in the planning of maximizing soil carbon storage in sustainable land-use management and policy development. Five InVEST soil carbon stock spatial models were produced for both agricultural landscapes, which showed great variation based on the data used to develop it. Aggregate carbon stock potentially stored in the landscape-scale study areas also varied between datasets used. Integrating soil sample data along with national carbon stock data shows prospective applicability in assessing contextual landscape-scale potential soil carbon stock storage.
Artificial Intelligence (AI) is set to redefine how farming occurs. Throughout history examples of technological advances have shown that less labour has been required on a farm while at the same time increasing the output of food production. However, an interesting observation is that as technology has improved the farming process and replaced workers, it has opened a new avenue known as diversification. This paper focuses specifically on the impact that AI will have on the future of farming in the European sector. The literature brings to light common trends that technological innovations have always decreased the number of workers required in the farming process while at the same time maximising efficiency. AI will also follow the same trend, however, instead of eliminating workers in the farming process soon, the present observations show that farmers will still require workers to work alongside AI. The reason for increased investments in AI is due to research data showing a decline in population growth in Europe and the struggling profitability of farmers. Thereby analysts believe that a labour shortage will occur, and industries will struggle to fill those skills requirements. A qualitative summary was done on artificial intelligence technologies’ development impact on the labour of the agricultural sector of Europe.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.