In regression testing, Test case prioritization (TCP) is a technique to arrange all the available test cases. TCP techniques can improve fault detection performance which is measured by the average percentage of fault detection (APFD). History-based TCP is one of the TCP techniques that consider the history of past data to prioritize test cases. The issue of equal priority allocation to test cases is a common problem for most TCP techniques. However, this problem has not been explored in history-based TCP techniques. To solve this problem in regression testing, most of the researchers resort to random sorting of test cases. This study aims to investigate equal priority in history-based TCP techniques. The first objective is to implement

In this work, an inventive photovoltaic evaporative cooling (PV/EC) hybrid system was constructed and experimentally investigated. The PV/EC hybrid system has the prosperous advantage of producing electrical energy and cooling the PV panel besides providing cooled-humid air. Two cooling techniques were utilized: backside evaporative cooling (case #1) and combined backside evaporative cooling with a front-side water spray technique (case #2). The water spraying on the front side of the PV panel is intermittent to minimize water and power consumption depending on the PV panel temperature. In addition, two pad thicknesses of 5 cm and 10 cm were investigated at three different water flow rates of 1, 2, and 3 lpm. In Case #1,

In this research, deposition of titanium oxide (TiO₂) and vanadium oxide (V₂O₅) thin film  in different mixing percentage  (0, 25 ,50, 75 and100)%   on the substrate of glass .The coating thickness was ( 50 nm  ).

In this research contact angle was measured and the effect of weather conditions. Results showed that the value of the contact angle of the prepared films reached its highest value at 50% (TiO₂+V₂O₅) was 160º.

The results showed that the optical transmittance of TiO₂ and V₂O₅ thin film decrease with increasing the deposition angle and decrease with increasing V₂O₅ pro

Background and Objectives: Dyspepsia is a disorder characterized by difficulty in digestion and represents a major health concern. Therefore, it is crucial to identify functional dyspepsia linked to Helicobacter pylori (H. pylori). This research aimed to determine the prevalence of H. pylori among patients with dyspepsia and to examine the potential risk factors associated with the infection. Materials and Methods: From August 14th to September 21st, 2024, a total of 105 patients with dyspepsia, who attended the Central Laboratory of Baghdad Medical City Complex (Iraq), were enrolled in this study. Data on nonsteroidal anti-inflam- matory drugs (NSAIDs), smoking, family history, fasting habits and frequent fast food consumption wer

Development and population expansion have the lion's share of driving up the fuel cost. Biodiesel has considerable attention as a renewable, ecologically friendly and alternative fuel source. In this study, CaO nanocatalyst is produced from mango leaves as a catalysis for the transesterification of waste cooking oil (WCO) to biodiesel. The mango tree is a perennial plant, and its fruit holds significant economic worth due to its abundance of vitamins and minerals. This plant has a wide geographical range and its leaves can be utilized without any negative impact on its growth and yield. An analysis was conducted to determine the calcium content in the fallen leaves, revealing a significant quantity of calcium that holds potential fo

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class