Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

Simplification of Resilient Modulus Testing for Subgrades

Resilient modulus has been used for characterizing the stress-strain behavior of subgrade soils subjected to traffic loadings in the design of pavements. With the recent release of the M-E Design Guide, highway agencies are further encouraged to implement the resilient modulus test to improve subgrade design. In the present study, physical property tests, unconfined compressive tests, resilient modulus (Mr) tests and Several Dynamic Cone Penetrometer (DCP) tests were conducted to assess the resilient and permanent strain behavior of 14 cohesive subgrade soils and five cohesionless soils encountered in Indiana. The applicability for simplification of the existing resilient modulus test, AASHTO T 307, was investigated by reducing the number of steps and cycles of the resilient modulus test. Results show that it may be possible to simplify the complex procedures required in the existing Mr testing to a single step with a confining stress of 2 psi and deviator stresses of 2, 4, 6, 8, 10 and 15 psi. Three models for estimating the resilient modulus are proposed based on the unconfined compressive tests. A predictive model to estimate material coefficients k1, k2, and k3 using 12 soil variables obtained from the soil property tests and the standard Proctor tests is developed. The predicted resilient moduli using all the predictive models compare satisfactorily with measured ones. A simple mathematical approach is introduced to calculate the resilient modulus. Although the permanent strain occurs during the resilient modulus test, the permanent behavior of subgrade soils is currently not taken into consideration. In order to capture both the permanent and the resilient behavior of subgrade soils, a constitutive model based on the Finite Element Method (FEM) is proposed. A comparison of the measured permanent strains with those obtained from the Finite Element (FE) analysis shows a reasonable agreement. An extensive review of the M-E design is done. Based on the test results and review of the M-E Design, implementation initiatives are proposed

Improvement of Saemangeum Dredged Soils Using Coffee Sludge for Vegetation Soil

In Korea, a large scale national project (Saemangeum Project) has been underway that requires a huge amount of dredged soils and their reclamation. Although a lot of dredged soil is needed for reclamation, only about 10% of the dredged soil is used. For this reason, much effort should be made to extensively use the dredged soil. The objective of the study is to find reasonable ways of improving the dredged soils in the Saemangeum area so that they can be used for vegetation of land plants. In order to develop ameliorating methods, we treated silty sand samples, the representative dredged soil of Saemangeum, with mountain soil (0% and 30%), sawdust fertilizer (0% and 6%), bioameliorant (0% and 6%), and coffee sludge (3%, 6%, and 9%), measured the germination rate of bent grass, and applied the lab experiment results to the field for validation. As a result, it was verified that when a mixture of coffee sludge and sawdust fertilizer was used, the chemical and physical properties of dredged soil were significantly improved. This implies that the beneficial use of the dredged soil can be facilitated

Instrumentation and Axial Load Testing of Displacement Piles

Despite the fact that results of many instrumented pile load tests have been reported in the literature, it is difficult to find well-documented instrumentation procedures that can be used when planning a load testing programme. A load test programme designed to investigate various aspects of the design and behaviour of driven steel piles is discussed in the present paper. Although the literature contains information on load testing of instrumented piles driven in either sand or clay, limited information is available regarding their axial load response in transitional soils (soils composed of various amounts of clay, silt and sand). Results are presented for fully instrumented axial load tests performed on an H pile and a closed-ended pipe pile driven into a multilayered soil profile consisting of transitional soils. In addition, the load testing planning, the instrumentation of the piles, the testing methods and the interpretation of the pile testing data are discussed in detail in the context of this and other load testing programmes described in the literature, in order to illustrate the various steps

Evaluation of Resilient Modulus of Subgrade and Base Materials in Indiana and Its Implementation in MEPDG

In order to implement MEPDG hierarchical inputs for unbound and subgrade soil, a database containing subgrade MR, index properties, standard proctor, and laboratory MR for 140 undisturbed roadbed soil samples from six different districts in Indiana was created. The MR data were categorized in accordance with the AASHTO soil classifications and divided into several groups. Based on each group, this study develops statistical analysis and evaluation datasets to validate these models. Stress-based regression models were evaluated using a statistical tool (analysis of variance (ANOVA)) and Z-test, and pertinent material constants (k1, k2 and k3) were determined for different soil types. The reasonably good correlations of material constants along with MR with routine soil properties were established. Furthermore, FWD tests were conducted on several Indiana highways in different seasons, and laboratory resilient modulus tests were performed on the subgrade soils that were collected from the falling weight deflectometer (FWD) test sites. A comparison was made of the resilient moduli obtained from the laboratory resilient modulus tests with those from the FWD tests. Correlations between the laboratory resilient modulus and the FWD modulus were developed and are discussed in this paper

Satellite-based In-situ Monitoring of Space Weather:

Many recent satellites have mission periods longer than 10 years; thus, satellite-based local space weather monitoring is becoming more important than ever. This article describes the instruments and data applications of the Korea Space wEather Monitor (KSEM), which is a space weather payload of the GeoKompsat-2A (GK-2A) geostationary satellite. The KSEM payload consists of energetic particle detectors, magnetometers, and a satellite charging monitor. KSEM will provide accurate measurements of the energetic particle flux and three-axis magnetic field, which are the most essential elements of space weather events, and use sensors and external data such as GOES and DSCOVR to provide five essential space weather products. The longitude of GK-2A is 128.2° E, while those of the GOES satellite series are 75° W and 135° W. Multi-satellite measurements of a wide distribution of geostationary equatorial orbits by KSEM/GK-2A and other satellites will enable the development, improvement, and verification of new space weather forecasting models. KSEM employs a service-oriented magnetometer designed by ESA to reduce magnetic noise from the satellite in real time with a very short boom (1 m), which demonstrates that a satellite-based magnetometer can be made simpler and more convenient without losing any performance

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As the Arctic melt ponds play an important role in determining the interannual variation of the sea ice extent and changes in the Arctic environment, it is crucial to monitor the Arctic melt ponds with high accuracy. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which is the NASA's latest altimeter satellite based on the green laser (532 nm), observes the global surface elevation. When compared to the CryoSat-2 altimetry satellite whose along-track resolution is 250 m, ICESat-2 is highly expected to provide much more detailed information about Arctic melt ponds thanks to its high along-track resolution of 70 cm. The basic products of ICESat-2 are the surface height and the number of reflected photons. To aggregate the neighboring information of a specific ICESat-2 photon, the segments of photons with 10 m length were used. The standard deviation of the height and the total number of photons were calculated for each segment. As the melt ponds have the smoother surface than the sea ice, the lower variation of the height over melt ponds can make the melt ponds distinguished from the sea ice. When the melt ponds were extracted, the number of photons per segment was used to classify the melt ponds covered with open-water and specular ice. As photons are much more absorbed in the water-covered melt pondsthan the melt ponds with the specular ice, the number of photons persegment can distinguish the water- and ice-covered ponds. As a result, the suggested melt pond detection method was able to classify the sea ice, water-covered melt ponds, and ice-covered melt ponds. A qualitative analysis was conducted using the Sentinel-2 optical imagery. The suggested method successfully classified the water- and ice-covered ponds which were difficult to distinguish with Sentinel-2 optical images. Lastly, the pros and cons of the melt pond detection using satellite altimetry and optical images were discussed

Interaction between an Eco-Spiral Bolt and Crushed Rock in a Borehole Evaluated by Pull-Out Testing

The interactions between an eco-spiral bolt and crushed rocks in a borehole were evaluated by pull-out testing in a laboratory and numerical analysis. The porosity of the crushed rock surrounding the bolt depended on the size of the eco-spiral bolt and affected the eco-spiral bolt’s axial resistance force. The axial resistance force and the porosity of the crushed rocks in the borehole showed an inverse relationship. The porosity was also related to the size of the eco-spiral bolt. The maximum principal stress between the bolt and the rock was related to the porosity of the crushed rock and the size difference between the eco-spiral bolt and the borehole. At low porosity the experimental and numerical analyses show similar relationships between the axial resistance force and the displacement. However, at high porosity, the numerical results deviated greatly from the experimental observation. The initial agreement is attributed to the state of residual resistance after the maximum axial resistance force, and the latter divergence was due to the decreasing axial resistance force owing to slippage

Identification of MYC as an antinecroptotic protein that stifles RIPK1-RIPK3 complex formation

The underlying mechanism of necroptosis in relation to cancer is still unclear. Here, MYC, a potent oncogene, is an antinecroptotic factor that directly suppresses the formation of the RIPK1-RIPK3 complex. Gene set enrichment analyses reveal that the MYC pathway is the most prominently down-regulated signaling pathway during necroptosis. Depletion or deletion of MYC promotes the RIPK1-RIPK3 interaction, thereby stabilizing the RIPK1 and RIPK3 proteins and facilitating necroptosis. Interestingly, MYC binds to RIPK3 in the cytoplasm and inhibits the interaction between RIPK1 and RIPK3 in vitro. Furthermore, MYC-nick, a truncated form that is mainly localized in the cytoplasm, prevented TNF-induced necroptosis. Finally, down-regulation of MYC enhances necroptosis in leukemia cells and suppresses tumor growth in a xenograft model upon treatment with birinapant and emricasan. MYC-mediated suppression of necroptosis is a mechanism of necroptosis resistance in cancer, and approaches targeting MYC to induce necroptosis represent an attractive therapeutic strategy for cancer