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Machine learning and tectonic setting determination:Bridging the gap between Earth scientists and data scientists

Geoscience Frontiers 2024年第1期15卷 339-353页

作者： pratchaya takaew Jianhong Cecilia Xia Luc S.Doucet Department of Spatial Sciences School of Earth and Planetary SciencesCurtin University6845 PerthWAAustralia Earth Dynamics Research Group School of Earth and Planetary SciencesCurtin University6845 PerthWAAustralia State Key Laboratory of Geological Processes and Mineral Resources China University of GeosciencesWuhanHubei 430074China

Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies,because current methods are designed using limited number of data and cannot handle large *** geoscience,tectonic discrimination illustrates this issue,using geochemical analyses to define tectonic settings when most of the geological record is missing,which is the case for most of the older portion of the Earth’s *** are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings,and their chemical compositions can be an effective way to understand tectonics-related mantle ***,the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique ***,weathering,erosion,and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic *** address those limitations,supervised machine learning techniques(a part of artificial intelligence)are being utilized more often as a tool to analyze multidimensional datasets and statistically process data to tackle big data *** contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine *** learning,especially Convolutional Neural Network(CNN)is the most accurate ***,it requires a large dataset and considerable processing time,and the gain of accuracy can be at the expense of ***,this study designed guidelines for data pre-processing,tectonic setting classification and objectively evaluating the model *** also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research,aimed at closing the divide between earth scientists and data scie

关键词： Machine learning Artificial intelligence Geochemistry Igneous petrology Basalts Tectonic settings

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Preparation of glutinous rice starch/polyvinyl alcohol copolymer electrospun fibers for using as a drug delivery carrier

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Asian Journal of Pharmaceutical Sciences 2018年第3期13卷 239-247页

作者： Patthanakorn Jaiturong Busaban Sirithunyalug Sukum Eitsayeam Chawalinee Asawahame pratchaya Tipduangta Jakkapan Sirithunyalug Department of Pharmaceutical Sciences Faculty of Pharmacy Chiang Mai University Department of Physics and Materials Faculty of Science Chiang Mai University Department of Industrial Pharmacy Faculty of Pharmaceutical Sciences Huachiew Chalermprakiet University

Glutinous rice starch(GRS) is commonly produced in the Northeast of Thailand. GRS is a biopolymer which is widely used in the food industry but not yet commonly applied within the pharmaceutical industry as an alternative resource. GRS exhibits a branch chain structure which is not feasible to fabricate as nanofiber. Therefore, combining GRS with polyvinyl alcohol(PVA) in hybrid form can be a potential platform to produce GRS-PVA *** nanofibers of 2%(w/v) GRS combined with 8%(w/v) PVA were fabricated by an electrospinning process. A scanning electron microscope(SEM) revealed an average diameter size of the GRS-PVA nanofibers equal to 191 ± 25 nm. A highly water soluble model drug,Chlorpheniramine maleate(CPM), was incorporated into the GRS-PVA electrospun fibers to prove a drug delivery carrier concept and drug release control of the nanofibers. The GRSPVA nanofibers exhibited a biphasic CPM release in which approximately 60% of the drug immediately released in 10 min, and it reached 90% drug release in 120 min. This study demonstrated a potential application of GRS combining with PVA as an oral drug delivery ***, it can be a promised step that expands the application GRS in pharmaceuticals and related areas.

关键词： Electrospinning Drug release Nanofibers Biomaterials Glutinous rice starch

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