Comparison of Cross-Entropy, Dice, and Focal Loss for Sea Ice Type Segmentation

Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generation of ice maps for large regions. However, results from CNN models still need to undergo scrutiny as higher metrics performance not always translate to adequate outputs. Sea ice type classes are imbalanced, requiring special treatment during training. We evaluate how three different loss functions, some developed for imbalanced class problems, affect the performance of CNN models trained to predict the dominant ice type in Sentinel-1 images. Despite the fact that Dice and Focal loss produce higher metrics, results from cross-entropy seem generally more physically consistent

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

2013) Impact of trade and Human capital on Economic growth of India: An empirical analysis. The Romanian Economic

Trade is the principal channel through which the flow of idea

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons

Study on the Effect of Laser Welding Parameters on the Microstructure and Mechanical Properties of Ultrafine Grained 304L Stainless Steel

In the present study, an ultrafine grained (UFG) 304L stainless steel with the average grain size of 300 nm was produced by a combination of cold rolling and annealing. Weldability of the UFG sample was studied by Nd: YAG laser welding under different welding conditions. Taguchi experimental design was used to optimize the effect of frequency, welding time, laser current and laser pulse duration on the resultant microstructure and mechanical properties. X-ray Diffraction (XRD), Optical Microscope (OM), Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), microhardness measurements and tension tests were conducted to characterize the sample after thermomechanical processing and laser welding. The results showed that the ultrafine grained steel had the yield strength of 1000 Mpa and the total elongation of 48%, which were almost three times higher than those of the as-received sample. The microstructure of the weld zone was shown to be a mixture of austenite and delta ferrite. The microhardness of the optimized welded sample (315 HV0.5) was found to be close to the UFG base metal (350 HV). It was also observed that the hardness of the heat affected zone (HAZ) was  lower than that of the weld zone, which was related to the HAZ grain growth during laser welding. The results of optimization also showed that the welding time was the most important parameter affecting the weld strength. Overall, the study showed that laser welding could be an appropriate and alternative welding technique for the joining of UFG steels

Prevalence of Chondrocalcinosis in Patients above 50 Years and the Relationship with Osteoarthritis

Background: Some studies showed a relation between chondrocalcinosis and osteoarthritis (OA). Hence, considering the importance of chondrocalcinosis diagnosis andnecessity for its integration with OA, the current study aims at investigating prevalence of chondrocalcinosis in patients above 50 years admitted to Isfahan Al-Zahra Medical Center and its relationship with OA. Materials and Methods: In a cross-sectional study, 600 patients who referred to the radiology units of Al-Zahra Hospital for radiography of different joints were studied during 2013–2014. The patients images were studied for chondrocalcinosis and OA by a radiologist and also examined clinically and results of imaging by an expert rheumatologist. The prevalence of chondrocalcinosis and it relation with OA was determined by Statistical Package for Social Sciences software and using of Chi-square and t-test. Results: 23 patients under study had chondrocalcinosis (3.83%). patients with chondrocalcinosis had higher age average, and they were in age group of 70 years and older, but no significant difference was observed regarding the prevalence of the disease in both genders. Chondrocalcinosis prevalence in terms of body mass index showed significant differences (P = 0.001). All patients with chondrocalcinosis had a history of joint disease and prevalence of chondrocalcinosis in terms of joint disease history showed a significant difference (P < 0.001). Conclusion: Prevalence of chondrocalcinosis is relatively high in the Iranian population of 70 years and older. Hence, more investigation considering to the diagnosis of chondrocalcinosis among patients with OA is very important

Effect of graphene oxide and friction stir processing on microstructure and mechanical properties of Al5083 matrix composite

In this study, the surface nanocomposite containing graphene oxide was produced on the Al5083 alloy surface, using Friction Stir Processing (FSP) in liquid cooled condition, in order to improve the microstructure and mechanical properties. For this purpose, FSP was carried out up to 3 passes on a base alloy with and without reinforcing particles. Microstructural features and mechanical properties of the obtained surface nanocomposite, FSPed Al 5083 and base alloy were investigated. In order to study the microstructure, Electron Back Scatter Diffraction (EBSD) was used. It was revealed that the grain size nanocomposite was about 1 μm after the process. This was while the grain size of the specimen with no reinforcement, after the process was 6 ± 1.1 μm and the size of the base alloy was 23 ± 2.3 μm. The substantial effect of the reinforcing particles in preventing the grains growth in the nanocomposite specimen was the main reason for this difference. Study of mechanical properties of base alloy, FSPed specimen, and the nanocomposite revealed that the simultaneous use of cooling environment and performing the process, increased the hardness of stir zone compared to the base alloy. This increase was raised in the presence of graphene oxide particles and reached to 123 ± 1.7 HV. It was also observed that the nanocomposite had a better tensile behavior than the base alloy and the FSPed specimen. SEM images of the fracture surfaces indicated the existence of dimples and voids at the surface of the base alloy specimens and the FSPed specimen which showed their ductile fracture, but at the nanocomposite surface, in addition to the ductile fracture, a brittle fracture was occurred

Cognitive Predictors of Cousin Marriage Among Couples Visiting Counseling Centers in Kohgiluyeh-Boyer Ahmad Province

Background and Objectives: Giving birth to a child with disabilities is two-three times more likely in consanguineous marriages. Due to the various negative consequences of such marriages, this study aimed to determine the cognitive predictors of consanguineous marriages. Materials and Methods: In this cross-sectional study, convenience sampling was applied to select 516 people who visited four different marriage counseling centers in Kohgiluyeh-Boyer Ahmad Province. A self-report questionnaire was administered to collect data. Bivariate correlations and logistic regression analysis were performed to analyze the data in SPSS-20. Results: The mean age of the respondents was 23.43 ± 3.96 years (range: 15-30 years). About 43.4% of the participants had married a relative. Regression analysis suggested subjective norms (OR = 1.304) and cultural factors (OR = 1.244) as the best predictors of cousin marriage. Conclusion: Considering the high rate of cousin marriage in the studied population, it is pre-marriage genetic counseling seems essential. Designing educational interventions on subjective norms and cultural factors related to cousin marriage may also be useful in reducing the rates of cousin marriages