Clinico-Pathological Measures and Management of Mucinous Ovarian Cancer: Single Institutional Study

Background: Mucinous ovarian cancer is less common and less aggressive epithelial ovarian cancer, which represents 3% of epithelial ovarian cancer. Patient and methods: A retrospective descriptive analysis involving all patients with mucinous ovarian cancer who underwent surgical management or referred to National Cancer Institute (NCI), Cairo University (CU), from January 2010 to January 2020. Of 46 cases reviewed. 16 cases were excluded (10 cases had metastatic and 6 cases had incomplete data). Results: forty-six cases reviewed with median age of 48.3±18.1 years. Half of patients were in premenopausal status. Patients presented with distention with or without pain were 70% of cases, 10% of patients with abdominal mass and 6.7% of patients with bleeding. Most of cases 63.30% were diagnosed at late stages (1C and beyond). Patients had unilateral disease were 70% of cases and had elevated tumor markers were 53.3 % of cases. The most commonly elevated tumor marker was CA 125 (26.7%). Patients had comorbidities were 30% of cases. Surgery was the main line of management. Total abdominal hysterectomy and bilateral salpingo-oophorectomy plus infracolic omentectomy were done in 73.3% of patients, 6.7% underwent total abdominal hysterectomy and bilateral salpingo-oophorectomy, 3.3% underwent cytoreductive surgery and 3.3% underwent cytoreductive surgery and 13% of patients underwent fertility sparing surgery. The role of pelvic lymphadenectomy was limited (13.3% underwent pelvic lymphadenectomy and only one patient had positive lymph node metastasis). Conclusion: The most important prognostic factors were disease stage, laterality, tumor markers and performance status

Impacts of symmetrical and asymmetrical voltage sags on DFIG-based wind turbines considering phase-angle jump, voltage recovery, and sag parameters

This paper presents a new analysis into the impacts of various symmetrical and asymmetrical voltage sags on doubly fed induction generator (DFIG)-based wind turbines. Fault ride-through requirements are usually defined by the grid codes at the point of common coupling (PCC) of wind farms to the power network. However, depending on the network characteristics and constraints, the voltage sag conditions experienced at the wind generator terminals can be significantly different from the conditions at the PCC. Therefore, it is very important to identify the voltage sags that can practically affect the operation of wind generators. Extensive simulation studies are carried out in MATLAB/Simulink to investigate the transient overshoots and ripples that appear in the rotor current and dc-link voltage when the DFIG is subjected to various types of (a)symmetrical faults. For the first time, the impacts of phase-angle jump and operational constraints of circuit breakers are examined. Furthermore, the influences of sag parameters including type, initial point-on-wave instant, depth, and impedance angle are investigated. Complementary theoretical analyses are also presented to support the validity of observations made in the simulation studies

Machine Learning in Model-Based Forest Height Inversion

Model-based (PM) forest height inversion from Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) measurements is today an established application demonstrated and validated at large scales for a wide variety of boreal and tropical forest sites at different frequencies (from X- down to P- band) ​[1], [2]​. Although the estimation performance obtained may depend on the individual observation spaces in each case, it is generally (very) convincing. However, as with any model-based inversion approach, there are inherent limitations that can restrict expected performance depending on the individual case ​[3]​

Magnetic localization and control of helical robots for clearing superficial blood clots

This work presents an approach for the localization and control of helical robots during removal of superficial blood clots inside in vitro and ex vivo models. The position of the helical robot is estimated using an array of Hall-effect sensors and precalculated magnetic field map of two synchronized rotating dipole fields. The estimated position is used to implement closed-loop motion control of the helical robot using the rotating dipole fields. We validate the localization accuracy by visual feedback and feature tracking inside the in vitro model. The experimental results show that the magnetic localization of a helical robot with diameter of 1 mm can achieve a mean absolute position error of 2.35 ± 0.4 mm (n = 20). The simultaneous localization and motion control of the helical robot enables propulsion toward a blood clot and clearing at an average removal rate of 0.67 ± 0.47 mm3/min. This method is used to localize the helical robot inside a rabbit aorta (ex vivo model), and the localization accuracy is validated using ultrasound feedback with a mean absolute position error of 2.6 mm

Airport service performance at Abu Dhabi International Airport

To acquire a significant footing in today’s competitive airport environments, enhancing airport service performance for passengers is essential. This study aims to investigate the relationship between queuing time, prime services, security screening, and service performance at Abu Dhabi International Airport in the United Arab Emirates (UAE). A quantitative methodology was employed. The sample size for the PLS-SEM analysis and the passengers’ airport service performance was determined to be 230 respondents. The results revealed a significant relationship between queuing time, prime services, security screening, and airport service performance. The findings also demonstrated a significant positive relationship between queuing time and airport service performance (β = 0.193, t = 3.564, p ≤ 0.000), a significant positive relationship between prime services and airport service performance (β = 0.478, t = 9.225, p ≤ 0.000), and a significant positive relationship between security screening and airport service performance (β= 0.227, t = 4.196, p ≤ 0.000). The outcomes are anticipated to support Abu Dhabi International Airport management in making efficient processes to augment airport service accomplishment for passengers from UAE and different countries

Assessment of uncertainties in projected temperature and precipitation over the Arabian Peninsula using three categories of CMIP5 multimodel ensembles

Background: Projections of temperature and precipitation with low uncertainties are key parameters to climate change related studies. Purpose: The projected temperature and precipitation and their uncertainties over the Arabian Peninsula for the 21st century for three CMIP5 multimodel ensembles under RCP4.5 and RCP8.5 are examined in this paper. Methods: Analyzing the performance of 30 CMIP5 model individually, they are categorized into three groups for the present climate (1976–2005). By applying simple model averaging ensemble method, three multimodel ensemble means, namely: (i) all CMIP5 models ensemble (AME), (ii) selected CMIP5 models ensemble (SME), and (iii) best-performing CMIP5 models ensemble (BME) are developed. Results Over the Arabian Peninsula, a continuous rise in temperature is obtained in all three ensembles (i.e., AME, SME, and BME) in the 21st century. The BME shows enhanced changes in temperature at the end of 21st century as compared to AME and SME. Moreover, the BME shows a remarkable reduction in uncertainties for the projected temperature. The AME, SME, and BME show strong inter-annual variability for the projected precipitation over the peninsula. Compared to AME and SME, the BME revealed enhanced positive change in the annual mean precipitation by the end of 21st century. Conclusions: Regionally, southern/northwestern areas of the peninsula receive enhanced/reduced future precipitation as compared to the present climate. The diferences in the projected precipitation and temperature signals increase largely between the three ensembles towards the end of 21st century. Therefore, it is concluded that selecting the best-performing models may lead a better planning by the policy makers and stakeholder for the region

Assessment of uncertainties in projected temperature and precipitation over the Arabian Peninsula a comparison between different categories of CMIP3 models

Background: This paper examined the level of uncertainties in precipitation and temperature simulations by Coupled Model Intercomparison Project Phase 3 (CMIP3) over the Arabian Peninsula. Purpose: Different techniques are employed to assess the ranges of uncertainties in projected temperature and precipitation over the Arabian Peninsula. Methods: For the present climate (1970–1999), the 22 CMIP3 models are grouped into four out of which two main categories, i) all models ensemble and ii) best performing models ensemble, are used to assess the uncertainties in the future temperature and precipitation over the Arabian Peninsula. Results: The CMIP3 ensemble projections for the above two main categories revealed a continuous increase in temperature over the peninsula during the 21st century. For the period 2070–2099, the all (best performing) models ensemble revealed an increase in temperature by 2.32 ± 2.45 (3.85 ± 1.54), 3.49 ± 2.49 (4.91 ± 1.61), and 3.28 ± 1.47 (5.36 ± 1.47) C, relative to the present climate, under the B1, A1B, and A2 scenario, respectively, while the intermodel ranges are projected to be from -3.36 to 6.08 (0.84 to 5.96), -2.26 to 7.68 (1.94 to 7.29), and -1.79 to 7.40 (2.75 to 7.10) C, respectively. Meanwhile, for the same period, the annual precipitation is projected to increase by 5.16 ± 30 (3.2 ± 25), 10.48 ± 34 (1.82 ± 28), and 15.29 ± 43 (5.3 ± 32)%, relative to the present climate under the B1, A1B, and A2 scenario, while the intermodel ranges are projected to be from -94 to 265 (-71 to 175), -95 to 322 (-74 to 205), and -95 to 375 (-75 to 235)%, respectively, for all (best performing) models ensemble. Conclusion The uncertainty of projected temperature and precipitation is reduced in the best performing models ensemble compared to the all models. At annual scale, surplus (deficit) precipitation pattern is projected across southern and southwestern (northern and northwestern) parts of the peninsula. The above results indicate that a better choice of models from the CMIP3 database could reduce the uncertainty range associated with future projections over the Arabian Peninsula

Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (>0.60) in most cities except the southeast of Nantong City (AOD 0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (>0.9) and had a minimum in December (<0.4) for all the cities. Frequencies of 0.3 ≤ AOD < 0.4 and 0.4 ≤ AOD < 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD

Classification of aerosols over Saudi Arabia from 2004–2016

Knowledge of aerosol size and composition is very important for investigating the radiative forcing impacts of aerosols, distinguishing aerosol sources, and identifying harmful particulate types in air quality monitoring. The ability to identify aerosol type synoptically would greatly contribute to the knowledge of aerosol type distribution at both regional and global scales, especially where there are no data on chemical composition. In this study, aerosol classification techniques were based on aerosol optical properties from remotely-observed data from the Ozone Monitoring Instrument (OMI) and Aerosol Robotic Network (AERONET) over Saudi Arabia for the period 2004–2016 and validated using data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). For this purpose, the OMI-based Aerosol Absorption Optical Depth (AAOD) and Ultra-Violet Aerosol Index (UVAI), and AERONET-based AAOD, Ångström Exponent (AE), Absorption Ångström Exponent (AAE), Fine Mode Fraction (FMF), and Single Scattering Albedo (SSA) were obtained. Spatial analysis of the satellite-based OMI-AAOD showed the dominance of absorbing aerosols over the study area, but with high seasonal variability. The study found significant underestimation by OMI AAOD suggesting that the OMAERUV product may need improvement over bright desert surfaces such as the study area. Aerosols were classified into (i) Dust, (ii) Black Carbon (BC), and (iii) Mixed (BC and Dust) based on the relationships technique, between the aerosol absorption properties (AAE, SSA, and UVAI) and size parameters (AE and FMF). Additionally, the AE vs. UVAI and FMF vs. UVAI relationships misclassified the aerosol types over the study area, and the FMF vs. AE, FMF vs. AAE and FMF vs. SSA relationships were found to be robust. As expected, the dust aerosol type was dominant both annually and seasonally due to frequent dust storm events. Also, fine particulates such as BC and Mixed (BC and Dust) were observed, likely due to industrial activities (cement, petrochemical, fertilizer), water desalination plants, and electric energy generation. This is the first study to classify aerosol types over Saudi Arabia using several different aerosol property relationships, as well as over more than one site, and using data over a much longer time-period than previous studies. This enables classification and recognition of specific aerosol types over the Arabian Peninsula and similar desert regions

Skill assessment of Saudi-KAU and C3S models in prediction of spring season rainfall over the Arabian Peninsula

A skillful prediction of precipitation has great value, particularly for regions that suffer from water stress. In this study, we assess the potential predictability and skill of the Copernicus Climate Change Service (C3S) and SaudiKAU models in their simulation of precipitation over the Arabian Peninsula during spring (March–May) for the period 1993–2016. For this purpose, data from individual models as well as the multi-model ensemble (MME) is used. The prediction data for MAM precipitation initialized at Feb (Lead 1), Jan (Lead 2), and Dec (Lead 3), were obtained from the 5 C3S and Saudi-KAU coupled global climate model. The potential predictability was computed by evaluating the signal to noise ratio and the theoretical limit of correlation skill, while the prediction skill was estimated from the temporal anomaly correlation co-efficient. The results show that the Saudi-KAU, CMCC, and UKMO models have slightly higher potential predictability of about 0.25, 0.35, and 0.25 respec­ tively, as compared to other models. It is also observed that individual models as well as their MME show a high (low) potential predictability over southwestern (northern) regions of the Peninsula. Moreover, the Saudi-KAU, CMCC, and MME show a reasonably good correlation skill (0.68, 0.59, and 0.57) while the SEAS model displays lower skill (0.14) for spring precipitation. All model simulations reveal a decrease in prediction skill for longer lead times. On the other hand, the individual models and their MME successfully reproduced the Pacific (i.e. ENSO) teleconnection patterns while displaying lower skill over the tropical Atlantic Ocean. The results indicate that the model biases have negative impacts on potential predictability and prediction skill over the Arabian Peninsula during the spring season