Results 81 to 90 of about 105,180 (314)

Recuperação de solos afetados por sais pela aplicação de gesso de jazida e calcário no Nordeste do Brasil Reclamation of salt-affected soils in Northeast Brazil with application of mined gypsum and limestone

Revista Brasileira de Engenharia Agrícola e Ambiental, 2004
Objetivando-se avaliar o efeito da aplicação de corretivos químicos sobre as propriedades físicas e químicas, bem como na recuperação de solos afetados por sais do Perímetro Irrigado de Custódia, no Estado de Pernambuco, Brasil, realizou-se um ...
Maria de F. C. Barros, Maurício P. F. Fontes, Víctor H. Alvarez V., Hugo A. Ruiz   +3 more
doaj   +1 more source

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

Deep Underground Science and Engineering, EarlyView.
The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +1 more source

Students of Limestone School, ca. 1909

, 1909
Photograph of students of Limestone School, Limestone, Maine. Date estimated as 1909. Part of a collection donated by Agnes Beckwith, on behalf of her aunt Eunice Young, formerly Eunice Flood Coggins.https://digitalmaine.com/limestone_images/1024 ...
Frost Memorial Library, Limestone, Maine
core  

A comparison between primary grasslands and abandoned quarries of the magnesian limestone, county Durham, with special reference to scrub invasion [PDF]

, 1993
Seven grassland Sites of Special Scientific Interest on the Magnesian Limestone of County Durham were botanically surveyed using the methodology of the National Vegetation Classification.
Allchin, Elizabeth
core  

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Students of Limestone School, Limestone, Maine, ca. 1909

, 1909
Photo of students of Limestone School in front of the school building. Written on back, All my pupil but one. Ben is the tall boy at the left side of the window.
Frost Memorial Library, Limestone, Maine
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Performance assessment of a potential underground thermal storage facility: Case study of the closed mine Prosper‐Haniel in the Ruhr coal mining area in Germany

Deep Underground Science and Engineering, EarlyView.
This study demonstrates the feasibility of an underground closed‐loop thermal storage facility at a post‐mining site, intended for seasonal heat energy storage. Its principal design shows water flow directions in winter and summer (1, 2), heat pumps (3), an upper water reservoir (4), and connecting pipes (5).
Dmytro Rudakov, Oleksandr Inkin
wiley   +1 more source

Upper bound limit analysis of coral reef limestone cavern roof stability incorporating a tension‐shear failure mechanism with tensile‐strength cut‐off

Deep Underground Science and Engineering, EarlyView.
For the roof of coral reef limestone caverns, a novel tension‐shear composite failure mechanism was developed. The most critical tensile crack model was identified using a hybrid optimization algorithm, and the stability of the cavern roof was analyzed accordingly.
Dongsheng Xu, Chenxu Li, Chuantan Hou
wiley   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Limestone, Maine Dam in 1958

, 1958
Image of the Limestone, Maine dam in 1958.https://digitalmaine.com/limestone_images/1008/thumbnail ...
Frost Memorial Library, Limestone, Maine
core