Oklahoma State University–Stillwater

About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.

Soil aggregation and carbon sequestration are tightly correlated with the abundance of arbuscular mycorrhizal fungi: results from long-term field experiments

Abstract: We examined the role of arbuscular mycorrhizal fungi (AMF) in ecosystems using soil aggregate stability and C and N storage as representative ecosystem processes. We utilized a wide gradient in AMF abundance, obtained through long-term (17 and 6 years) large-scale field manipulations. Burning and N-fertilization increased soil AMF hyphae, glomalin-related soil protein (GRSP) pools and water-stable macroaggregates while fungicide applications reduced AMF hyphae, GRSP and water-stable macroaggregates. We found that AMF abundance was a surprisingly dominant factor explaining the vast majority of variability in soil aggregation. This experimental field study, involving long-term diverse management practices of native multispecies prairie communities, invariably showed a close positive correlation between AMF hyphal abundance and soil aggregation, and C and N sequestration. This highly significant linear correlation suggests there are serious consequences to the loss of AMF from ecosystems.

Observation of a centrality-dependent dijet asymmetry in lead-lead collisions at √sNN=2.76 Tev with the ATLAS detector at the LHC

Abstract: By using the ATLAS detector, observations have been made of a centrality-dependent dijet asymmetry in the collisions of lead ions at the Large Hadron Collider. In a sample of lead-lead events with a per-nucleon center of mass energy of 2.76 TeV, selected with a minimum bias trigger, jets are reconstructed in fine-grained, longitudinally segmented electromagnetic and hadronic calorimeters. The transverse energies of dijets in opposite hemispheres are observed to become systematically more unbalanced with increasing event centrality leading to a large number of events which contain highly asymmetric dijets. This is the first observation of an enhancement of events with such large dijet asymmetries, not observed in proton-proton collisions, which may point to an interpretation in terms of strong jet energy loss in a hot, dense medium.

Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network

Abstract: The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel-perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel-perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F-measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F-measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.

Recent advances in utilization of biochar

Abstract: Biomass thermochemical processes result in a common byproduct char. The char is also called biochar particularly when it is used as a soil amendment for soil health improvement. Effective utilization of biochar is critical for improving economic viability and environmental sustainability of biomass thermochemical technologies. Application of biochar for both agricultural and environmental benefits has been studied and reviewed extensively. However, there are limited reviews on other biochar applications, such as for catalysis and adsorption. This paper provides an overview of recent advances in several biochar utilizations including its use as catalyst, soil amendment, fuel cell, contaminant adsorbent, gas storage and activated carbon. Discussions on biochar production methods, properties and advanced characterization techniques are also provided. Biochar is a valuable resource, however, its effective utilization require further investigation of its structure and properties, and methods to modify those.