A Diagnostic Study of the Indian Ocean Dipole Mode in El Nino and Non- El Nino Years

The Indian Ocean Dipole Mode (IODM) is examined by comparing the characteristics of oceanic and atmospheric circulations, heat budgets, and possible mechanisms of IODM between El Nino and non-El Nino years. ERA-40 reanalysis data, Reynold SST, and ocean analysis from Modular Ocean Model with the assimilation of the temperature profile from World Ocean Dataset 1998 are used to form three-year composites of IODM during El Nino (72, 82, 97) and non-El Nino (61, 67, 94) years. In El Nino years, two off-equatorial, anti-cyclonic circulations develop as a Rossby-wave response to the increased pressure over the Indian Ocean. The resultant winds from easterlies to northeasterlies (from southerlies to southeasterlies) in the northwestern (southeastern) tropical Indian Ocean warms (cools) the mixed layer temperature by inducing an anomalous zonal (meridional and vertical) component in the ocean current that advects the basic-state mixed layer temperature. In non-El Nino years, a monsoon-like flow induces winds from westerlies to southwesterlies (from southerlies to southeasterlies) in the northwestern (southeastern) Indian Ocean. As a result, the cold advection by the anomalous eastward current (northward current) in the northwestern (southeastern) tropical Indian Ocean becomes dominant in non-El Nino years. In addition, the anomalous winds in these regions are the same sign as the climatological monthly mean winds. Hence the anomalous latent and sensible heat fluxes further contribute to the decrease of SST in the northwestern and the southeastern Indian Ocean. Consequently, the cooling of the eastern tropical Indian Ocean rather than the warming of western tropical Indian Ocean becomes the major feature of the IODM during non-El Nino years

Hair Trace Element and Electrolyte Content in Women with Natural and In Vitro Fertilization-Induced Pregnancy

The objective of the present study was to perform comparative analysis of hair trace element content in women with natural and in vitro fertilization (IVF)-induced pregnancy. Hair trace element content in 33 women with IVF-induced pregnancy and 99 age- and body mass index-matched control pregnant women (natural pregnancy) was assessed using inductively coupled plasma mass spectrometry. The results demonstrated that IVF-pregnant women are characterized by significantly lower hair levels of Cu, Fe, Si, Zn, Ca, Mg, and Ba at p < 0.05 or lower. Comparison of the individual levels with the national reference values demonstrated higher incidence of Fe and Cu deficiency in IVF-pregnant women in comparison to that of the controls. IVF pregnancy was also associated with higher hair As levels (p < 0.05). Multiple regression analysis revealed a significant interrelation between IVF pregnancy and hair Cu, Fe, Si, and As content. Hair Cu levels were also influenced by vitamin/mineral supplementation and the number of pregnancies, whereas hair Zn content was dependent on prepregnancy anthropometric parameters. In turn, planning of pregnancy had a significant impact on Mg levels in scalp hair. Generally, the obtained data demonstrate an elevated risk of copper, iron, zinc, calcium, and magnesium deficiency and arsenic overload in women with IVF-induced pregnancy. The obtained data indicate the necessity of regular monitoring of micronutrient status in IVF-pregnant women in order to prevent potential deleterious effects of altered mineral homeostasis

A Diagnostic Study of the Indian Ocean Dipole Mode in El Nino and Non- El Nino Years

The Indian Ocean Dipole Mode (IODM) is examined by comparing the characteristics of oceanic and atmospheric circulations, heat budgets, and possible mechanisms of IODM between El Nino and non-El Nino years. ERA-40 reanalysis data, Reynold SST, and ocean analysis from Modular Ocean Model with the assimilation of the temperature profile from World Ocean Dataset 1998 are used to form three-year composites of IODM during El Nino (72, 82, 97) and non-El Nino (61, 67, 94) years. In El Nino years, two off-equatorial, anti-cyclonic circulations develop as a Rossby-wave response to the increased pressure over the Indian Ocean. The resultant winds from easterlies to northeasterlies (from southerlies to southeasterlies) in the northwestern (southeastern) tropical Indian Ocean warms (cools) the mixed layer temperature by inducing an anomalous zonal (meridional and vertical) component in the ocean current that advects the basic-state mixed layer temperature. In non-El Nino years, a monsoon-like flow induces winds from westerlies to southwesterlies (from southerlies to southeasterlies) in the northwestern (southeastern) Indian Ocean. As a result, the cold advection by the anomalous eastward current (northward current) in the northwestern (southeastern) tropical Indian Ocean becomes dominant in non-El Nino years. In addition, the anomalous winds in these regions are the same sign as the climatological monthly mean winds. Hence the anomalous latent and sensible heat fluxes further contribute to the decrease of SST in the northwestern and the southeastern Indian Ocean. Consequently, the cooling of the eastern tropical Indian Ocean rather than the warming of western tropical Indian Ocean becomes the major feature of the IODM during non-El Nino years.UnpublishedJCR Journalope

The Ninth Visual Object Tracking VOT2021 Challenge Results

The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time"short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website1

The Eighth Visual Object Tracking VOT2020 Challenge Results

[[abstract]]The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).[[notice]]補正完

The Tenth Visual Object Tracking VOT2022 Challenge Results

The Visual Object Tracking challenge VOT2022 is the tenth annual tracker benchmarking activity organized by the VOT initiative. Results of 93 entries are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2022 challenge was composed of seven sub-challenges focusing on different tracking domains: (i) VOT-STs2022 challenge focused on short-term tracking in RGB by segmentation, (ii) VOT-STb2022 challenge focused on short-term tracking in RGB by bounding boxes, (iii) VOT-RTs2022 challenge focused on “real-time” short-term tracking in RGB by segmentation, (iv) VOT-RTb2022 challenge focused on “real-time” short-term tracking in RGB by bounding boxes, (v) VOT-LT2022 focused on long-term tracking, namely coping with target disappearance and reappearance, (vi) VOT-RGBD2022 challenge focused on short-term tracking in RGB and depth imagery, and (vii) VOT-D2022 challenge focused on short-term tracking in depth-only imagery. New datasets were introduced in VOT-LT2022 and VOT-RGBD2022, VOT-ST2022 dataset was refreshed, and a training dataset was introduced for VOT-LT2022. The source code for most of the trackers, the datasets, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net )