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1. Expedition 380 Scientific Prospectus: NanTroSEIZE Stage 3: Frontal Thrust Long-Term Borehole Monitoring System (LTBMS)

   https://doi.org/10.14379/iodp.sp.380.2017  

   Becker, K.; Kinoshita, M.; Toczko, S. (August 2017, Scientific prospectus)

   null (Ed.)

   The Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) program is a coordinated, multiexpedition drilling project designed to investigate fault mechanics and seismogenesis along subduction megathrusts through direct sampling, in situ measurements, and long-term monitoring in conjunction with allied laboratory and numerical modeling studies. The fundamental scientific objectives of the NanTroSEIZE drilling project include characterizing the nature of fault slip and strain accumulation, fault and wall rock composition, fault architecture, and state variables throughout the active plate boundary system. International Ocean Discovery Program (IODP) Expedition 380 will deploy a permanent long-term borehole monitoring system (LTBMS) in a new cased hole at Site C0006 above the frontal thrust, where previous expeditions have conducted logging-while-drilling and coring operations. This deployment will be the third borehole observatory deployed as part of the NanTroSEIZE program, and it will extend the existing NanTroSEIZE LTBMS network seaward to include the frontal thrust region of the Nankai accretionary prism. This expedition will cover a period of 40 days, beginning on 12 January 2018 and ending on 24 February. The LTBMS sensors will include seafloor reference and formation pressure sensors, a thermistor string, a broadband seismometer, a tiltmeter, a volumetric strainmeter, geophones, and accelerometers. The casing plan does not include a screened interval for this LTBMS; the monitoring zone will be isolated from the seafloor by a swellable packer (inside the casing) and cement at the casing shoe. This LTBMS will be later linked to the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) submarine network. This Scientific Prospectus outlines the scientific rationale, objectives, and operational plans for Site C0006. A congruent “NanTroSEIZE Investigation at Sea” will convene researchers aboard the D/V Chikyu to use the latest techniques and equipment to reexamine cores, shipboard measurement data (including X-ray computed tomography scans), and logging-while-drilling data collected during NanTroSEIZE Stage 1 in 2007. 

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2. Responding to the demands of big data scientific instruments through the development of an international software defined exchange point (SDX)

   Ibarra, J.; Bezerra, J.; Lopez, L.F.; Morgan, L; Cox, D. (April 2016, UbuntuNet Connect-Connect 2016)

   Science is being conducted in an era of information abundance. The rate at which science data is generated is increasing, both in volume and variety. This phenomenon is transforming how science is thought of and practiced. This transformation is being shaped by new scientific instruments that are being designed and deployed that will dramatically increase the need for large, real-time data transfers among scientists throughout the world. One such instrument is the Square Kilometer Array (SKA) being built in South Africa that will transmit approximately 160Gbps of data from each radio dish to a central processor. This paper describes a collaborative effort to respond to the demands of big data scientific instruments through the development of an international software defined exchange point (SDX) that will meet the network provisioning needs for science applications. This paper discusses the challenges of end-to-end path provisioning across multiple research and education networks using OpenFlow/SDN technologies. Furthermore, it refers to the AtlanticWave-SDX, a project at Florida International University and the Georgia Institute of Technology, funded by the US National Science Foundation (NSF), along with support from Brazil’s NREN, Rede Nacional de Ensino e Pesquisa (RNP, and the Academic Network of Sao Paulo (ANSP). Future work explores the feasibility of establishing an SDX in West Africa, in collaboration with regional African RENs, based on the planned availability of submarine cable spectrum for use by research and education communities. 

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3. pyDARN: A Python software for visualizing SuperDARN radar data

   https://doi.org/10.3389/fspas.2022.1022690  

   Shi, Xueling; Schmidt, Marina; Martin, Carley J.; Billett, Daniel D.; Bland, Emma; Tholley, Francis H.; Frissell, Nathaniel A.; Khanal, Krishna; Coyle, Shane; Chakraborty, Shibaji; et al (December 2022, Frontiers in Astronomy and Space Sciences)

   The Super Dual Auroral Radar Network (SuperDARN) is an international network of high frequency coherent scatter radars that are used for monitoring the electrodynamics of the Earth’s upper atmosphere at middle, high, and polar latitudes in both hemispheres. pyDARN is an open-source Python-based library developed specifically for visualizing SuperDARN radar data products. It provides various plotting functions of different types of SuperDARN data, including time series plot, range-time parameter plot, fields of view, full scan, and global convection map plots. In this paper, we review the different types of SuperDARN data products, pyDARN’s development history and goals, the current implementation of pyDARN, and various plotting and analysis functionalities. We also discuss applications of pyDARN, how it can be combined with other existing Python software for scientific analysis, challenges for pyDARN development and future plans. Examples showing how to read, visualize, and interpret different SuperDARN data products using pyDARN are provided as a Jupyter notebook. 

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4. Modern approaches for leveraging biodiversity collections to understand change in plant-insect interactions

   https://doi.org/10.3389/fevo.2022.924941  

   Balmaki, Behnaz; Rostami, Masoud A.; Christensen, Tara; Leger, Elizabeth A.; Allen, Julie M.; Feldman, Chris R.; Forister, Matthew L.; Dyer, Lee A. (August 2022, Frontiers in Ecology and Evolution)

   Research on plant-pollinator interactions requires a diversity of perspectives and approaches, and documenting changing pollinator-plant interactions due to declining insect diversity and climate change is especially challenging. Natural history collections are increasingly important for such research and can provide ecological information across broad spatial and temporal scales. Here, we describe novel approaches that integrate museum specimens from insect and plant collections with field observations to quantify pollen networks over large spatial and temporal gradients. We present methodological strategies for evaluating insect-pollen network parameters based on pollen collected from museum insect specimens. These methods provide insight into spatial and temporal variation in pollen-insect interactions and complement other approaches to studying pollination, such as pollinator observation networks and flower enclosure experiments. We present example data from butterfly pollen networks over the past century in the Great Basin Desert and Sierra Nevada Mountains, United States. Complementary to these approaches, we describe rapid pollen identification methods that can increase speed and accuracy of taxonomic determinations, using pollen grains collected from herbarium specimens. As an example, we describe a convolutional neural network (CNN) to automate identification of pollen. We extracted images of pollen grains from 21 common species from herbarium specimens at the University of Nevada Reno (RENO). The CNN model achieved exceptional accuracy of identification, with a correct classification rate of 98.8%. These and similar approaches can transform the way we estimate pollination network parameters and greatly change inferences from existing networks, which have exploded over the past few decades. These techniques also allow us to address critical ecological questions related to mutualistic networks, community ecology, and conservation biology. Museum collections remain a bountiful source of data for biodiversity science and understanding global change. 

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5. Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study

   https://doi.org/10.1186/s40537-023-00815-3  

   Rostami, Masoud A.; Balmaki, Behnaz; Dyer, Lee A.; Allen, Julie M.; Sallam, Mohamed F.; Frontalini, Fabrizio (December 2023, Journal of Big Data)

   Abstract Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approach to classify pollen grains in the Great Basin Desert, Nevada, USA. Our dataset consisted of 10,000 images of 40 pollen species. To mitigate the limitations imposed by the small volume of our training dataset, we conducted an in-depth comparative analysis of numerous pre-trained Convolutional Neural Network (CNN) architectures utilizing transfer learning methodologies. Simultaneously, we developed and incorporated an innovative CNN model, serving to augment our exploration and optimization of data modeling strategies. We applied different architectures of well-known pre-trained deep CNN models, including AlexNet, VGG-16, MobileNet-V2, ResNet (18, 34, and 50, 101), ResNeSt (50, 101), SE-ResNeXt, and Vision Transformer (ViT), to uncover the most promising modeling approach for the classification of pollen grains in the Great Basin. To evaluate the performance of the pre-trained deep CNN models, we measured accuracy, precision, F1-Score, and recall. Our results showed that the ResNeSt-110 model achieved the best performance, with an accuracy of 97.24%, precision of 97.89%, F1-Score of 96.86%, and recall of 97.13%. Our results also revealed that transfer learning models can deliver better and faster image classification results compared to traditional CNN models built from scratch. The proposed method can potentially benefit various fields that rely on efficient pollen identification. This study demonstrates that DL approaches can improve the accuracy and efficiency of pollen identification, and it provides a foundation for further research in the field. 

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