Results 51 to 60 of about 45,399 (264)
(Fig. 5) Lithology of sediment core GeoB13071-1
, 2012 (Fig. 5) Lithology of sediment core GeoB13071-
Guillermo Francés (6968897) +11 more
core +1 more source
Deep Underground Science and Engineering, EarlyView.In response to the problem of strong mining pressure manifestation along the goaf roadway in a thick coal seam mining area, this study uses a new type of combined blasting roof cutting technology to effectively control the manifestation and deformation of mining pressure along the goaf roadway.
Xiaoding Xu +6 more
wiley +1 more source
(Fig. 4) Lithology of sediment core GeoB11035-1
, 2012 (Fig. 4) Lithology of sediment core GeoB11035-
Guillermo Francés (6968897) +11 more
core +1 more source
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
Microbial biogeography of arctic streams: exploring influences of lithology and habitat
Frontiers in Microbiology, 2012 Terminal restriction fragment length polymorphism and 16S rRNA gene sequencing were used to explore the community composition of bacterial communities in biofilms on sediments (epipssamon) and rocks (epilithon) in stream reaches that drain watersheds ...
Julia R. Larouche +4 more
doaj +1 more source
(Fig. 6) Lithology of sediment core GeoB130206-1
, 2012 (Fig. 6) Lithology of sediment core GeoB130206-
Guillermo Francés (6968897) +11 more
core +1 more source
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
Transactions of the Karelian Research Centre of the Russian Academy of Sciences, 2015 New results of pollen analysis for a lake in the Umba River catchment (southern Kola Peninsula) are presented. The lake was surveyed within a study of lake sediments carried out to determine the relative White Sea shore displacements in the Late Glacial ...
Tatiana Sapelko +2 more
doaj +1 more source
Distribution of Selected Soil Nutrients in Aggregate Sizes of Soils Under Different Geologic Formations in Southeast Nigeria [PDF]
Bulgarian Journal of Soil Science, 2019 Soil nutrients which are major factors to crop yield and food security could be loss to erosion as a result of structural break down leading to water pollution and low crop yield.
Leonard Chimaobi Agim +6 more
doaj +1 more source
Modular artificial neural network for prediction of petrophysical properties from well log data [PDF]
, 1996 This paper reports the application of Kohonen's Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms, and the commonly used Back Propagation Neural Network (BPNN) to the prediction of petrophysical properties from well log data ...
Wong, K.W. +4 more
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