Thermal analysis of a flat-plate collector in multiphase flows, including superheat

A thermal analysis of the performance of a solar flat-plate collector operating in nonboiling, boiling, and superheated regimes is presented. The performance of the collector under these single and multiphase conditions is governed by the axial fractional channel lengths of the subcooled (nonboiling) and the superheated regions. The overall thermal loss coefficient, the dimensionless capacitance rate, and collector efficiency factors for various collector operating regions are defined. A new "Generalized Heat Removal Factor," Fs, and a new overall thermal loss coefficient, UL, for flat-plate collectors under any operation mode are developed. The thermal efficiency a flat-plate collector, whether under nonboiling, boiling, or superheated conditions, is evaluated using, Fs and UL. It is shown that the value of Fs decreases and the value of UL increases as the degree of superheat increases. Current applications of flat-plate collectors having multiphase flows are represented by those charged with refrigerants.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27542/1/0000586.pd

Thermal analysis of a flat-plate boiling collector having sub-cooled inlet and saturated exit states

The analysis of the thermal performance of a boiling flat-plate solar collector is presented. A generalized heat removal factor and a new formulation for the overall thermal loss coefficient are developed. It is demonstrated that the conventional heat removal factor for non-boiling collectors is a limiting case of a more generalized result. The new formulation for the overall thermal loss coefficient is shown to be a function of the fractional non-boiling length of the flow channel. The influence of the inlet sub-cooling is evaluated and the operating limits of solar flat-plate collectors are determined. A comparison is made between the thermal model for boiling collectors having sub-cooled inlet states and experimental results. Favorable agreement is obtained.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28137/1/0000588.pd

A thermal-optical analysis of a compound parabolic concentrator for single and multiphase flows, including superheat

A thermal and optical analysis of the performance of a refrigerant charged Compound Parabolic Concentrator (CPC) for solar applications operating in non-boiling, boiling and super-heated regimes is presented. The performance of the CPC working under these single and multiphase conditions is governed by the axial fractional channel lengths of the non-boiling and the superheating regions. The overall thermal loss coefficient, the dimensionless capacitance rate and collector efficiency factors for various CPC operating regions are defined. A new “Generalized Heat Removal Factor“, ℱ s for solar collectors under any operation mode is developed. The thermal efficiency of a CPC and flat-plate collector, whether under non-boiling, boiling or superheated conditions, is evaluated using ℱ s which enables the selection of a suitable collector design and concentration ratio at some specified operational temperature. It is shown that, in general, a CPC has a greater thermal conversion efficiency than a flat-plate for a given operating condition. Es wird eine thermische und optische Analyse des Verhaltens eines Verbund-Parabol-Kollektors für die Anwendung der Sonnenenergie vorgestellt, der mit Kältemittel im nichtsiedenden, und übehitzten Bereich arbeitet. Das Verhalten dieses unter ein- und mehrphasigen Bedingungen arbeitenden Kollektors wird bestimmt durch den axialen Anteil der Kühl-kanallängen im nichtsiedenden und im überhitzten Zustand. Es werden der mittlere thermische Verlustkoeffizient, die dimensionslose Wärmekapazität sowie die Kollektorwirkungsgrade für verschiedene Zustandsbereiche dieses Parabolspiegels definiert. Ein neuer „verallgemeinerter Wärmeabflußfaktor“, ℱ s , für Sonnenkollektoren, die unter beliebigen Betriebsbedingungen arbeiten, wurde entwickelt. Mit diesem ℱ s Faktor werden der thermische Wirkungsgrad des Parabolkollektors und eines Platten-kollektors bei einphasiger flüssiger Strömung beim Sieden und für überhitzten Dampf berechnet, wodurch es möglich wird, eine geeignete Kollektorauslegung und das dazugehörige Konzentrationsverhältnis bei vorgegebenen Betriebstemperaturen zu wählen. Es wird gezeigt, daß im allgemeinen der parabolische Kollektor einen höheren thermischen Wirkungsgrad besitzt als der Platten-kollektor bei identischen Betriebsbedingungen.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46656/1/231_2005_Article_BF01377577.pd

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

دراسات تطوريه مقارنة على جهاز القنوات الجانبية في سمكتي المبروك سيبرينس كاربيو والجامبوزيا جامبوزيا أفينس أفينس

The lateral line canals and the neuromasts situated inside their lumina are described in two fishes differing in their feeding habits. There are 20 canal neuromasts in the bottom feeder Cyprinus carpio, while in the surface feeder Gambusia affinis affinis there are 8 neuromasts only. Thus the canal system is well developed in the first species than in the second one. The canals open to the exterior through fine canaliculi with fine pore which are also great in number in Cyprinus carpio.تم دراسة وتوزيع قنوات الخط الجانبي لسمكتي المبروك والجمبوزيا حيث وجد ان جهاز الخط الجانبي متطور جداً ومعقد في المبروك ، بينما هذا الجهاز مختزل وبسيط في الجامبوزيا

A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data

Abstract Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients