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Jamming is electromagnetic radiation or reflection that impairs the function of electronic instruments and equipment or communication tools. Intentionally disrupting or interfering with GPS signals, which are used for positioning, navigation, and timing, known as “GPS jamming”, is accomplished using a radio frequency emitting device. On January 8, 2022 (the day of a NATO exercise), it was investigated how GPS signal jamming affected the position accuracy at three IGS points in Iceland. The obtained coordinate differences between kinematic processing and static processing reached values of about 0.5–10 meters for the MAYV, and HOFN stations in this study. In addition to GPS signal jamming effect in Iceland, horizontal and vertical velocity fields of the three IGS stations in Iceland covering a twenty-two year period (2000–2022) in this study. According to the obtained results, a motion of about 2cm–2.5cm per year (horizontal) and 0.1cm–2.1cm per year (vertical) was computed at the three IGS stations (HOFN, REYK, and MAYV) located in Iceland.

The Mw 7.3 Tonga earthquake occurred on 11 November 2022 at 19°.288 S and 172°.147 W. It was caused by reverse faulting in the outer rise of the Pacific Plate, about 75 km east of the Tonga Trench. We studied the Tonga earthquake on 11 November 2022 in order to detect a violent eruption of the Tonga submarine volcano in the South Pacific. Submarine volcano eruptions can displace seawater in a number of different ways, potentially triggering tsunamis. The Tonga subduction zone has the highest rate of plate convergence on Earth. It is one of the places with the most earthquakes. However, the recorded thrust events that can be placed with certainty on the plate boundary haven't been stronger than M 8.0, and the area's history suggests that there isn't much seismic coupling along the arc. The modelling of this earthquake based on the assumption that the fault plane dips to the west give dimensions of about 50 × 35 km, with most of the distance down-dipping from the hypocenter. In our study, we used the CSRS-PPP software to process TONGA station data using static and kinematic methods. This study shows the horizontal coordinate differences of the TONGA station (static-kinematic), which range from a few millimetres to about 40 centimetres. [ABSTRACT FROM AUTHOR]

Objective: We aimed to assess the frequency of Y-chromosome microdeletions (YCMs) in a non-multiethnic urban population in our region, define predictive factors, and determine a new clinical threshold for YCMs in infertile men. Materials and Methods: A total of 281 patients with a sperm concentration =5 million/mL were retrospectively evaluated. Oligozoospermic and/or azoospermic patients with a sperm concentration of =5 million/mL were screened for the YCM analysis. Results: Y-chromosome microdeletion was detected in 9 (3.2%) of the 281 patients. All patients with YCM were azoospermic. The presence of azoospermia, a high folliclestimulating hormone level, and a high luteinizing hormone level were found to be important determinants for the identification of a microdeletion (P = .002, P = .002, and P = .021, respectively). If the presence of azoospermia and a sperm concentration threshold of <1 million/mL had been applied for the YCM test, the number of tests performed would have been reduced by 54.4% (153 tests) and 42.7% (120 tests), respectively, resulting in cost saving of approximately $11 474 and $9000, respectively. Conclusion: We recommend that the threshold for sperm concentration for YCM analysis be set at <1 million in individuals in developed countries and only in patients with azoospermia in developing countries, in order to reduce costs and save labor by excluding unnecessary tests. These proposed thresholds (azoospermia and sperm counts less than <1 million/mL) provide cost-effectiveness by significantly reducing the number of genetic tests ordered without affecting the diagnosis rate. [ABSTRACT FROM AUTHOR]

Copyright of SPORMETRE: The Journal of Physical Education & Sport Sciences / Beden Eğitimi ve Spor Bilimleri Dergisi is the property of SPORMETRE: The Journal of Physical Education & Sport Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Türkiye Jeoloji Bülteni is the property of TMMOB JEOLOJI MUHENDISLERI ODASI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The physical properties of fruit and vegetable products play an important role in their quality classification. With the existing manual methods, disease, pesticide and quality status of agricultural products are checked during the control stages. Manual sorting and classification of products requires expertise and the process is time consuming and labor intensive. Today, with the developing technology, the processing and marketing of products can be realized in optimum time and efficiency with the software techniques used in the fields of agriculture and food. In this study, date palm fruit, which has an important share in the fruit and vegetable market, is considered. The use of image processing and artificial intelligence techniques in classifying the quality of date fruits makes the sales process more consistent and efficient. Within the scope of the study, four different artificial intelligence techniques were used with a uniquely prepared dataset. The dataset consists of three different classes: good, bad and medium quality dates. The dataset specially prepared for the study was trained with MobileNetV2, ResNetV2-50, DenseNet- 201 and Inception-v3 deep learning models. Among the four models used in the study, MobileNetV2 model was determined as the most successful model with 95% accuracy rate. [ABSTRACT FROM AUTHOR]

The Global Positioning System (GPS) is a GNSS constellation, hut GNSS is not always GPS. GPS is one of the GNSS constellations used around the world. The GNSS constellations include; GPS (US), QZSS (Japan), Bcidon/BDS (China), Galileo (EU), and GLONASS (Russia). In 1999, the European Commission (EC) proposed the; European Galileo satellite navigation system for the; first time;. A four-phase; development was propose«!, including publie; and private; sector finance. Galileo was intended for both civilian and government use;, and is managed and controlled by civil authorities. Galileo is made up of 30 satellites, a numbe;r erf globally distributee! ground stations, mid a ground control anel monitoring system, all erf which are; extremely similar to the; structure, format, anel layout erf GPS. In this study, we; investigate; GPS/GLO NASS/Galileo/Bcidou/IRNSS/QZSS Navigation Satellite System integration algorithm for long base;line;s ranging from 1500 km te) 3000 km in China, Japan anel Mongolia. The; positioning performance with GPS/GLONASS/Galile;e)/BDS/IRNSS/QZSS, GPS-only, Galileo-only, GLONASS-only anel BDS-only, etc. is compared in terms erf the; positioning accuracy. An improvement erf' petitioning accuracy over long baselines can be; found with GPS/GLONASS/Galilco/BDS/QZSS/IRNSS compared with that erf' GPS-only anel that erf BDS-only. The; obtained differences erf' the; two baselines (Topcon Magnet Tools Software; (Multi-GNSS) -(CSRS-PPP (GPS/GLONASS), (Trimblc-RTX (GPS/GLONASS), (AUSPOS (GPS/GLONASS)) Online; Processing Software) by using GPS/GLONASS/Galilce)/BDS/QZSS/IRNSS signals is between 40 cm anel 111.5 cm on three; days. [ABSTRACT FROM AUTHOR]