The atmospheres of mars, venus and jupiter

Planetary atmosphere composition, temperature, and pressure of Mars, Venus, and Jupite

Results of the Mariner 6 and 7 Mars occultation experiments

Final profiles of temperature, pressure, and electron density on Mars were obtained for the Mariner 6 and 7 entry and exit cases, and results are presented for both the lower atmosphere and ionosphere. The results of an analysis of the systematic and formal errors introduced at each stage of the data-reduction process are also included. At all four occulation points, the lapse rate of temperature was subdadiabatic up to altitudes in excess of 20 km. A pronounced temperature inversion was present above the surface at the Mariner 6 exit point. All four profiles exhibit a sharp, superadiabatic drop in temperature at high altitudes, with temperatures falling below the frost point of CO2. These results give a strong indication of frozen CO2 in the middle atmosphere of Mars

Emergence of hexatic and long-range herringbone order in two-dimensional smectic liquid crystals : A Monte Carlo study

Using a high resolution Monte Carlo simulation technique based on multi-histogram method and cluster-algorithm, we have investigated critical properties of a coupled XY model, consists of a six-fold symmetric hexatic and a three-fold symmetric herringbone field, in two dimensions. The simulation results demonstrate a series of novel continues transitions, in which both long-range hexatic and herringbone orderings are established simultaneously. It is found that the specific-heat anomaly exponents for some regions in coupling constants space are in excellent agreement with the experimentally measured exponents extracted from heat-capacity data near the smecticA-hexaticB transition of two-layer free standing film

Towards the Application of Uncertainty Analysis in Architectural Design Decision-Making

To this day, proper handling of uncertainties -including unknown variables in primary stages of a design, an actual climate data, occupants` behavior, and degradation of material properties over the time- remains as a primary challenge in an architectural design decision-making process. For many years, conventional methods based on the architects' intuition have been used as a standard approach dealing with uncertainties and estimating the resulting errors. However, with buildings reaching great complexity in both their design and material selections, conventional approaches come short to account for ever-existing but unpredictable uncertainties and prove incapable of meeting the growing demand for precise and reliable predictions. This study aims to develop a probability-based framework and associated prototypes to employ uncertainty analysis and sensitivity analysis in architectural design decision-making. The current research explores an advanced physical model for thermal energy exchange characteristics of a hypothetical building and uses it as a test case to demonstrate the proposed probability-based analysis framework. The proposed framework provides a means to employ uncertainty and sensitivity analysis to improve reliability and effectiveness in a buildings design decision-making process

Fungal infections in patients with chronic liver disease: mortality and associated risk factors

Background: Patients with chronic liver disease are immunocompromised and prone to different opportunistic infections. Fungal infections in patients admitted with liver cirrhosis are not rare and they may increase mortality and morbidity of these patients. Aims of the study is to determine the mortality and its risk factors associated with fungal infections in patients with chronic liver disease.Methods: In this retrospective study, patients admitted with chronic liver disease during the last four years on this hospital were studied for diagnosed fungal infections. A matched control group of cirrhosis patients with a ratio of 1:2 admitted without fungal infections was also studied and mortality was compared between the two groups.Results: Seventy admitted patients of liver cirrhosis with microbial and histopathological evidence of fungal infection were found while 140 patients of the control group had no evidence of fungal infection. Hepatitis C virus infection was the major cause of cirrhosis (65%) and most of the patients were in child class C(63%). Urinary tract infection, esophageal candidiasis, and mucormycosis were major fungal infections. Mortality was much higher in the fungal infections group (34.3%) as compared to the non-infectious group (16%). On multivariate analysis, high WBCs count, hypo-albuminemia and high creatinine levels were the worst factors affecting mortality.Conclusions: Fungal infections are a significant cause of morbidity and mortality in patients with decompensated cirrhosis. Advanced cirrhosis, renal insufficiency, and leucocytosis are independent predictors of fatal outcome in these patients

Experimental Study of PV Panel Performance Using Backside Water Cooling Chamber

Received: 10 December 2022. Accepted: 28 March 2023.The authors would like to express their appreciation to the staff of the research center laboratory at Erbil Polytechnic University, Erbil, Iraq.Due to high solar irradiation and the high ambient temperature in Iraq, the solar cell temperature rises, and the electrical power output drops accordingly. In this study, an experimental photovoltaic (PV) panel prototype was developed to study the PV module's performance and power production efficiency. The developed photovoltaic module uses a water-cooling chamber for cooling. This experimental study uses a water-cooling system chamber technique at the rear side of the PV panel. The cooling system solar panel is a closed cycle, and the cooling water contacts the panel directly through the rear side of the PV panel using different flow rates. The results showed that the electrical efficiency increased by 10.42%, 11.87%, 13.77%, 18.1%, and 19.72% when mass flow rates of 1.5, 2, 2.5, 3, and 3.5 l/min, respectively, were used. The thermal efficiency at 1.5 and 3.5 l/min is 49.7% and 79.2%, respectively

Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond

This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond. The first study of its kind, we found that Bayesian Neural Networks (BNNs) and intelligent training techniques allowed our models to maintain performance amidst significant data shifts. Our results emphasize the importance of developing robust AI models capable of matching or surpassing clinician predictions, even under challenging conditions. Our exploration of model explainability revealed that stochastic models generate more diverse and personalized explanations thereby highlighting the need for AI models that provide detailed and individualized insights in real-world clinical settings. Furthermore, we underscored the importance of quantifying uncertainty in AI models which enables clinicians to make better-informed decisions based on reliable predictions. Our study advocates for prioritizing implementation science in AI research for healthcare and ensuring that AI solutions are practical, beneficial, and sustainable in real-world clinical environments. By addressing unique challenges and complexities in healthcare settings, researchers can develop AI models that effectively improve clinical practice and patient outcomes