A theoretical and experimental investigation was carried out to study the behavior of a two-phase closed thermosyphon loop (TPCTL) during steady-state operation using different working fluids. Three working fluids were investigated, i.e., distilled water, methanol, and ethanol. The TPCTL was constructed from an evaporator, condenser, and two pipelines (riser and downcomer). The driving force is the difference in pressure between the evaporator and condenser sections and the fluid returns to the heating section by gravity. In this study, the significant parameters used in the experiments were filling ratios (FR%) of 50%, 75%, and 100% and heat-input range at the evaporator section of 215-860.2 W. When the loop reached to

 Background: It is evident that there is a lack of clear consensus on the role of luteal phase serum Progesterone (P) level in the prediction of early pregnancy after controlled ovarian hyperstimulation (COH) protocols in assisted reproductive techniques (ART).
Objective: We conducted this study in order to investigate the potential value of luteal phase serum progesterone measurement, in women undergoing ICSI treatment cycles and receiving progesterone supplements, in relation to pregnancy rate.
Patients: A total of 68 women aged 20-40 years undergoing their first intracytoplasmic sperm injection (ICSI) cycles in fertility and I.V.F center of Kamal Al samrai hospital.
Methods: women consecutively treated by ICSI had Estima

Microencapsulated of paraffin wax which acts as core material of phase change
material covered by polymer was prepared by using rabid (physical-chemical) with lower
energy (green) method. Prepolymer of condensed Melamine-Formaldehyde resin, was
solidified by heat effect gradually and surrounds the Paraffin wax as microcapsules. The
diameter of the prepared capsules was about (170-220) micron which has a proportion with
the prepolymer temperature, otherwise the thermal analysis appears as a best value of
enthalpy (ΔH) which was (12 J/gm) when the prepolymer temperature was (60˚C)

The accumulation of sediment in reservoirs poses a major challenge that impacts the storage capacity, quality of water, and efficiency of hydroelectric power generation systems. Geospatial methods, including Geographic Information Systems (GIS) and Remote Sensing (RS), were used to assess Dukan Reservoir sediment quantities. Satellite and reservoir water level data from 2010 to 2022 were used for sedimentation assessment. The satellite data was used to analyze the water spread area, employing the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to enhance the water surface in the satellite imagery of Dukan Reservoir. The cone formula was employed to calculate the live storag

The characterizations of reservoir require reliable knowledge of certain fundamental reservoir properties. Log measurements can define or at least infer these properties: resistivity, porosity, shale volume, lithology, and water, oil, or gas saturation and permeability. The current study represents evaluation of petrophysical properties in well LU-12 for Zubair Formation in Luhais Oil Field, southern Iraq. The petrophysical evaluation was based on geophysical well logs data to delineate the reservoir characteristics of Zubair Formation. The available geophysical well logs such as (sonic, density, neutron, gamma ray, SP, and resistivity logs) are digitized using the Didger software. The environmental corrections and petrophysical paramete

Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a