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Purpose: The purpose of this paper is to analyze the correlation between the Twitter activity of two airline companies and their stock performance at the Istanbul Stock Exchange (BIST). Design/methodology/approach: Overall, 113,018 tweets were divided into 34,152 semantic and 78,866 share tweets. Semantic tweets are tweets mentioning company's products or services and were labeled manually and with deep learning models. Share tweets were divided into 13,618 relevant and 65,248 irrelevant tweets. Findings: A positive correlation was found between share tweets and stock performance. Semantic tweets did not display a correlation with stock performance. Relevant share tweets displayed as a strong correlation as all share tweets for one company. Also, the manual labeling of 8,000 tweets led to the discovery of many insights related to service provision in the airway industry, management of digital support channels, management of reputation on social media and using Twitter as a customer support platform. Practical implications: Relevant share tweets comprise only 20% of all share tweets for one company and show the same level of correlation with stock performance. This means that the efficiency of business intelligence solutions created to monitor Twitter activity can be improved five times by saving computational power, network bandwidth and data storage. Originality/value: Previous research has analyzed all Twitter activity taken together. By dividing tweets into semantic and share tweets, this paper illustrates that it is, in fact, share tweets that are correlated with stock performance and not semantic tweets. [ABSTRACT FROM AUTHOR]

Arctic sea-ice thickness and salinity retrievals are simulated to explore the performance of nadir-observing microwave radiometry operating with up to 16 frequency channels in the 0.5–2-GHz frequency range. A radiative transfer model is used to create lookup tables of the circularly polarized thermal emissions of first-year (FY) and multiyear (MY) sea ice, and the performance of two distinct retrieval methods is examined. The first method retrieves only sea-ice thicknesses, while the second retrieves both ice thickness and ice salinity. Retrieval errors are simulated for both FY and MY sea ice as a function of ice thickness, salinity, and temperature to investigate the impact of radiometric uncertainty, the frequency channels used, and any errors in ancillary information. To gain further insight into Arctic scale retrieval performance, a simulated brightness temperature dataset is produced for Arctic sea ice for the period October 2020–March 2021 using sea-ice thicknesses from the SMOS-CryoSat-2 algorithm. Compared to existing sea-ice thickness retrievals obtained from 1.4-GHz microwave radiometers, the results demonstrate that 0.5–2-GHz radiometry can achieve higher sensitivity to a sea-ice thickness within the range 0.5–1.5 m for FY sea ice and enable the retrieval of multiple sea-ice parameters (thickness and salinity) simultaneously. [ABSTRACT FROM AUTHOR]

Numerous studies have examined the growth rate and the level of development of countries. According to economy, sociology, and even history, it can be easily observed that knowledge is one of the most important indicators for countries to achieve sustainable growth and development. Education is seen as the main input for a society to be considered a "knowledge society," and the aim of this study is to investigate this multidimensional character of education. In the scope of the study, United Nations Public Administration Network (UNPAN)'s Knowledge Society Index is reviewed and recalculated with respect to different variables to understand the significance of being a knowledge society for the economic g rowth of a country. Regarding this recalculation, another important aim of the study is to rank Turkey in the recalculated index and try to expose reasons for its actual situation. In this context, Turkey's strengths and weaknesses are set out. In conclusion, in accordance with the determinations, policy recommendations to authorities are also included. [ABSTRACT FROM AUTHOR]

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the "KuKa" dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in "stare" nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters. Plain Language Summary: Data collected using a surface‐based radar instrument on sea ice during the MOSAiC Arctic expedition were used to develop new techniques to estimate the depth of the overlying snow. We used different polarizations of the radiation to detect the depths of the upper and lower snow surfaces, and subtracted them to give snow depth. These depths agreed well with an independently collected snow depth data set. Estimates of snow depth using two different radar frequencies were less accurate, whilst using information of the shape of the returning pulse of radiation also showed a relationship with the independent snow depths, though not as strong as the polarization method. These results indicate that polarimetry (using a new satellite mission) and/or waveform shape (using existing missions) could be used to estimate snow depth on sea ice from airborne or satellite platforms. Key Points: Novel polarization‐based snow depth estimation techniques were developed using surface‐based Ku‐ and Ka‐band polarimetric radar altimeter dataThe dominant scattering surface was the air/snow and snow/ice interface in co‐ and cross‐polarized data, respectively, at both frequenciesRadar‐derived snow depths agreed with independent measurements, with r2 up to 0.77 and accuracy of 1 cm for best‐performing techniques [ABSTRACT FROM AUTHOR]

An airborne microwave wide-band radiometer (500–2000 MHz) was operated for the first time in Antarctica to better understand the emission properties of sea ice, outlet glaciers and the interior ice sheet from Terra Nova Bay to Dome C. The different glaciological regimes were revealed to exhibit unique spectral signatures in this portion of the microwave spectrum. Generally, the brightness temperatures over a vertically homogeneous ice sheet are warmest at the lowest frequencies, consistent with models that predict that those channels sensed the deeper, warmer parts of the ice sheet. Vertical heterogeneities in the ice property profiles can alter this basic interpretation of the signal. Spectra along the lengths of outlet glaciers were modulated by the deposition and erosion of snow, driven by strong katabatic winds. Similar to previous experiments in Greenland, the brightness temperatures across the frequency band were low in crevasse areas. Variations in brightness temperature were consistent with spatial changes in sea ice type identified in satellite imagery and in situ ground-penetrating radar data. The results contribute to a better understanding of the utility of microwave wide-band radiometry for cryospheric studies and also advance knowledge of the important physics underlying existing L-band radiometers operating in space. [ABSTRACT FROM AUTHOR]

An ultrawideband radiometer was used to measure microwave brightness temperature spectra over Arctic sea ice in the Lincoln Sea near the north coast of Greenland. Spectra over the range of 0.5–2 GHz were compared to thermal infrared images collected during the airborne campaign and also compared to nearly concurrent Sentinel-1 C-band synthetic aperture radar (SAR) data. Based on those comparisons, spectral signatures were associated with thick multiyear ice and thin ice. A radiative transfer (RT) model consisting of a homogeneous slab of sea ice bounded by sea water and air was then used to invert the spectra for sea ice thickness and salinity. Inferred thicknesses were consistent with ice thickness climatology for ice floes in the Lincoln Sea. Salinities are higher than expected which may be a consequence of neglecting surface and volume scattering contributions in the models. [ABSTRACT FROM AUTHOR]