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Articles 1411 - 1440 of 2244
Full-Text Articles in Earth Sciences
Mgrre_Thinsections_Mgrre-142_76, Mgrre
Mgrre_Thinsections_Mgrre-142_85, Mgrre
Mgrre_Thinsections_Mgrre-143_2, Mgrre
Mgrre_Thinsections_Mgrre-145_1, Mgrre
Mgrre_Thinsections_Mgrre-146_3, Mgrre
Mgrre_Thinsections_Mgrre-141_23, Mgrre
Mgrre_Thinsections_Mgrre-141_78, Mgrre
Mgrre_Thinsections_Mgrre-141_35, Mgrre
Mgrre_Thinsections_Mgrre-141_38, Mgrre
Mgrre_Thinsections_Mgrre-141_57, Mgrre
Mgrre_Thinsections_Mgrre-141_75, Mgrre
Mgrre_Thinsections_Mgrre-141_85, Mgrre
Mgrre_Thinsections_Mgrre-142_14, Mgrre
Mgrre_Thinsections_Mgrre-142_45, Mgrre
Mgrre_Thinsections_Mgrre-142_57, Mgrre
Mgrre_Thinsections_Mgrre-142_64, Mgrre
Coral Reef Hydrodynamics: A Critical Review Of Roughness Indices And Their Characteristic Complexity, Faisal Karim, N. A. K. Nandasena, Lujain Kuruvachalil, Mohamed M. Mohamed, James P. Terry
Coral Reef Hydrodynamics: A Critical Review Of Roughness Indices And Their Characteristic Complexity, Faisal Karim, N. A. K. Nandasena, Lujain Kuruvachalil, Mohamed M. Mohamed, James P. Terry
All Works
This study provides a systematic review of hydrodynamic roughness in coral reef systems, focusing on drag coefficient (Cd), Manning's roughness coefficient (n), friction factor, and rugosity metrics. A structured bibliometric analysis was conducted using Scopus and Web of Science databases, including field measurements, laboratory flume experiments, and numerical studies validated with physical data. Results show substantial variability in roughness parameters across scales and environments. Reported drag coefficients range from 0.002 for smooth sandy beds to values exceeding 7.0 in complex mangrove-reef systems, with typical reef crest values between 0.05 and 0.30. Manning's n varies from 0.01 in smooth channels to …
Weed Seed Burial And Long-Term Viability Following Soil Inversion, Catherine Borger, Sultan Mia, Gaus Azam, Stephen Davies
Weed Seed Burial And Long-Term Viability Following Soil Inversion, Catherine Borger, Sultan Mia, Gaus Azam, Stephen Davies
Grain and Other Field Crops Research Articles
Strategic soil inversion is advocated as a periodic weed management strategy, typically recommended every 4 to 8 yr if the goal is to delay the evolution of herbicide resistance. It is assumed that the weed seeds buried during the inversion process will lose viability during this period. However, long-term seedbank persistence following soil inversion has not been investigated. If the buried seeds retain viability, subsequent inversions or other deep-tillage operations may return them to the soil surface, potentially replenishing the weed seedbank. The current study identified 30 sites within the wheatbelt of Western Australia (in 2020) that had previously been …
Using Geoscience To Empower A Community And Provide Access To Potable Groundwater, Hendratta Ali Dr
Using Geoscience To Empower A Community And Provide Access To Potable Groundwater, Hendratta Ali Dr
SACAD: Scholarly Activities
Access to potable water remains challenging around the world and remains one of the UN Sustainable Development Goals for 2030. Barriers to clean water access are exacerbated by the lack of technical expertise, limited funding for education and training, and the absence of reliable infrastructure that provides access to clean water. The Geoscientists Without Borders program, supported FHSU with funding to test, explore and provide access to clean potable groundwater to a rural community in Cameroon. We conducted water quality analyses, identified, mapped and located deep aquifers in fractured metamorphic rocks. Water quality analyses showed acidic pH and elevated amounts …
Mandibular Ecomorphology Of Tylosaurus (Reptilia: Mosasauridae), Andrew I. Price, Amanda Peng
Mandibular Ecomorphology Of Tylosaurus (Reptilia: Mosasauridae), Andrew I. Price, Amanda Peng
SACAD: Scholarly Activities
The large mosasaur Tylosaurus is known to have been a predator of large marine animals. Performing beam theory analysis, a computational technique that uses linear dimensions of a structure to calculate its resistance to bending, on the mandibles of six Tylosaurus specimens from the Smoky Hill Chalk of Kansas indicates dorsoventral and mediolateral bending strength both increase posteriorly along the mandible. Comparisons with Varanus komodoensis indicate relatively greater mediolateral bending strength mid-dentary in Tylosaurus, suggesting that resisting torsional forces of struggling prey was more important at this landmark in Tylosaurus and that more of its jaw length was used …
Mapping Of Petroleum-Prospective Structures In The Jurassic Complex Of The Western Aral Uplift Based On Seismic Attributes, Yuliya Leonidovna Salaydinova, Marina Georgievna Yuldasheva
Mapping Of Petroleum-Prospective Structures In The Jurassic Complex Of The Western Aral Uplift Based On Seismic Attributes, Yuliya Leonidovna Salaydinova, Marina Georgievna Yuldasheva
Technical science and innovation
To solve the problems of identifying and mapping prospective structures, comprehensive analysis of 3D seismic attributes is widely used at the present stage of geological research; it was applied to assess the petroleum potential of the Jurassic rock complex in the Aral Sea offshore area of the Ustyurt region. The study integrates structural mapping on the tops of Permo-Triassic and Jurassic horizons, attribute volumes (Variance/Coherence, Sweetness, root-mean-square (RMS) amplitude, Envelope) and generalized spectral decomposition (GSD, 19-41 Hz), combined with well-log and well-test data from the Western Aral, Meros, Dengiz and Karakum areas. Variance attributes were used to delineate fault zones …
Elemental And Mineralogical Characterization Of Permo-Carboniferous Coal In The Ngaka Coalfield, Southwest Tanzania: Implications For Coal Quality Variation, John W. Gama, Justina J. Saroni, Remigius L. Gama
Elemental And Mineralogical Characterization Of Permo-Carboniferous Coal In The Ngaka Coalfield, Southwest Tanzania: Implications For Coal Quality Variation, John W. Gama, Justina J. Saroni, Remigius L. Gama
Tanzania Journal of Science
Coal from heterolithic seams exhibits distinct chemical characteristics due to interbedded non-coal beds, which influence its quality and usability. Despite extensive research in these settings, the elemental and mineralogical characteristics of the heterolithic sequences of the Mchuchuma Formation (K2) in the Ngaka Coalfield remain poorly documented. This study provides the first detailed assessment of K2 seams across five active mine sites. Eleven representative samples were analyzed using proximate, elemental, and mineralogical techniques, supported by Pearson correlation, binary analysis, and Principal Component Analysis. Proximate analysis revealed that ash content varies across mine sites, with values ranging from 9.17 to 25.03%, volatile …
Mgrre_Thinsections_Mgrre-140_2, Mgrre
Mgrre_Thinsections_Mgrre-140_4, Mgrre
Mgrre_Thinsections_Mgrre-140_1, Mgrre
Mgrre_Thinsections_Mgrre-140_3, Mgrre
Mgrre_Thinsections_Mgrre-140_5, Mgrre
Applications Of Machine Learning In Enhancing Evaporation Estimation For Small Reservoirs: A Case Study In Semi-Arid South Texas, Syed Muhammad F Abdullah, Chu-Lin Cheng, Jude A. Benavides, Jungseok Ho, Rafael M. Almeida
Applications Of Machine Learning In Enhancing Evaporation Estimation For Small Reservoirs: A Case Study In Semi-Arid South Texas, Syed Muhammad F Abdullah, Chu-Lin Cheng, Jude A. Benavides, Jungseok Ho, Rafael M. Almeida
School of Earth, Environmental, & Marine Sciences Faculty Publications
Small reservoirs in semi-arid regions experience substantial evaporative losses but are rarely monitored at daily scales. A multi-reservoir machine learning (ML) framework was developed to estimate daily open-water evaporation. Empirical models (Penman, Penman-Monteith, Priestley-Taylor, Bowen Ratio Energy Budget) andabenchmark combination method (Daily Lake Evaporation Model-DLEM) were compared against ML models. Predictors combined gridded meteorology (gridMET) with reservoir attributes (surface area, average depth, maximum depth, and fetch). ML models (Random Forest-RF, Decision Tree-DT, K-Nearest Neighbor-KNN, and Support Vector Regression-SVR) were trained on four reservoirs using data from 2018 to 2025. Results from ML models were further validated using both DLEM and …
Machine Learning Based Models For Simulation And Analysis Of Bulk Earth Melt System, Abin Shakya
Machine Learning Based Models For Simulation And Analysis Of Bulk Earth Melt System, Abin Shakya
LSU Doctoral Dissertations
Understanding the segregation of bulk Earth melt systems into metallic (core) and silicate (mantle) phases under high-pressure and high-temperature conditions is central to modeling Earth’s interior, yet relevant experimental and computational studies remain limited. This work develops a machine learning–based simulation pipeline that iteratively couples first-principles (quantum mechanical) calculations with neural network training to generate high-fidelity force fields. Using major-element Fe–Mg–Si–O melt systems, with and without H and N, as testbeds, we demonstrate that this framework enables large-scale molecular dynamics simulations at near first-principles accuracy. We further introduce a sequence of phase identification methods, progressing from statistical binning of elemental …
Long-Term Assessment Of Cattail (Typha Spp.) Dynamics And Wetland Restoration In The Everglades Using Object-Based Landsat Time Series And Markov Modeling, Gideon Tandoh, Stephen Yankyera, Michael Sean Chenoweth
Long-Term Assessment Of Cattail (Typha Spp.) Dynamics And Wetland Restoration In The Everglades Using Object-Based Landsat Time Series And Markov Modeling, Gideon Tandoh, Stephen Yankyera, Michael Sean Chenoweth
Research, Publications & Creative Work
Assessing long-term vegetation response to restoration requires spatially consistent monitoring approaches that capture both observed change and likely future trajectories. In the Florida Everglades, nutrient enrichment contributed to the expansion of cattail (Typha spp.) within Water Conservation Area 3A (WCA-3A), raising concerns about restoration effectiveness. This study integrates a multi-temporal Landsat time series (2004–2024), object-based image analysis (OBIA), support vector machine (SVM) classification, and Markov transition modeling to quantify and project vegetation change in WCA-3A. Object-based segmentation reduced classification fragmentation in heterogeneous marsh landscapes, while SVM improved separation among spectrally similar vegetation classes. Classification accuracies remained consistently high across sensors …