The application of the test case prioritization method is a key part of system testing intended to think it through and sort out the issues early in the development stage. Traditional prioritization techniques frequently fail to take into account the complexities of big-scale test suites, growing systems and time constraints, therefore cannot fully fix this problem. The proposed study here will deal with a meta-heuristic hybrid method that focuses on addressing the challenges of the modern time. The strategy utilizes genetic algorithms alongside a black hole as a means to create a smooth tradeoff between exploring numerous possibilities and exploiting the best one. The proposed hybrid algorithm of genetic black hole (HGBH) uses the capabilities of considering the imperatives such as code coverage, fault finding rate and execution time from search algorithms in our hybrid approach to refine test cases considerations repetitively. The strategy accomplished this by putting experiments on a large-scale project of industrial software developed. The hybrid meta-heuristic technique ends up being better than the routine techniques. It helps in higher code coverage, which, in turn, enables to detect crucial defects at an early stage and also to allocate the testing resources in a better way. In particular, the best APFD value was 0.9321, which was achieved in 6 generations with 4.879 seconds the value to which the computer was run. Besides these, , the approach resulted in the mean value of APFD as 0.9247 and 0.9302 seconds which took from 10.509 seconds to 30.372 seconds. The carried out experiment proves the feasibility of this approach in implementing complex systems and consistently detecting the changes, enabling it to adapt to rapidly changing systems. In the end, this research provides us with a new hybrid meta-heuristic way of test case prioritization and optimization, which, in turn, helps to tackle the obstacles caused by large-scale test cases and constantly changing systems.
Focusing on the negative role of default risk on banks, as it is one of the most important risks facing banks, which are difficult to determine accurately, and its reflection on the indicators of profitability of cash flows. The increasing competition between banks led to an increase in the credit facilities granted by banks, and was accompanied by an increase in exposure to the risks of default, which led to an impact on the level of performance of banks in terms of achieving the required return according to the levels of high competition. Therefore, the problem of this study focused on the extent to which the risk indicators of default affect the profitability indicators of the cash flows of the banks research sample in the profit
... Show MoreThe aim of this paper is to derive a posteriori error estimates for semilinear parabolic interface problems. More specifically, optimal order a posteriori error analysis in the - norm for semidiscrete semilinear parabolic interface problems is derived by using elliptic reconstruction technique introduced by Makridakis and Nochetto in (2003). A key idea for this technique is the use of error estimators derived for elliptic interface problems to obtain parabolic estimators that are of optimal order in space and time.
The study discussed here deals with the isolation of Aspergillus niger from palm dates, the formal and the most famous fruit in Iraq, to test and qualify this fungus isolate for its ability to produce citric acid. Submerged fermentation technique was used in the fermentation process. A.niger isolated from “Zahdi” Palme dates was used in the study of the fermentation kinetics to get the production efficiency of citric acid. Kinetics of CA production via fermentation by A. niger S11 was evaluated within 432 h fermentation time and under submerged conditions of 11% (w/v) sucrose, 5% (v/v) inoculum size, pH 4, 30 °C and 150 rpm. The maximum citric acid produced was (37.116 g/l). Kine
This study investigated the application of the crystallization process for oilfield produced water from the East Baghdad oilfield affiliated to the Midland Oil Company (Iraq). Zero liquid discharge system (ZLD) consists of several parts such as oil skimming, coagulation/flocculation, forward osmosis, and crystallization, the crystallization process is a final part of a zero liquid discharge system. The laboratory-scale simple evaporation system was used to evaluate the performance of the crystallization process. In this work, sodium chloride solution and East Baghdad oilfield produced water were used as a feed solution with a concentration of 177 and 220 g/l. The impact of temperature (70, 80, and 90 °C), mixing speed (300, 400, and 500 rp
... Show MoreThis study investigated the application of the crystallization process for oilfield produced water from the East Baghdad oilfield affiliated to the Midland Oil Company (Iraq). Zero liquid discharge system (ZLD) consists of several parts such as oil skimming, coagulation/flocculation, forward osmosis, and crystallization, the crystallization process is a final part of a zero liquid discharge system. The laboratory-scale simple evaporation system was used to evaluate the performance of the crystallization process. In this work, sodium chloride solution and East Baghdad oilfield produced water were used as a feed solution with a concentration of 177 and 220 g/l. The impact of temperature (70, 80, and 90 °C), mixing speed (300, 400, and 500
... Show MoreA simple, fast, selective of a new flow injection analysis method coupled with potentiometric detection was used to determine vitamin B1 in pharmaceutical formulations via the prepared new selective membranes. Two electrodes were constructed for the determination of vitamin B1 based on the ion-pair vitamin B1-phosphotungestic acid (B1-PTA) in a poly (vinyl chloride) supported with a plasticized di-butyl phthalate (DBPH) and di-butyl phosphate (DBP). Applications of these ion selective electrodes for the determination of vitamin B1 in the pharmaceutical preparations for batch and flow injection systems were described. The ion selective membrane exhibited a near-Nernstian slope values 56.88 and 58.53 mV / decade, with the linear dy
... Show MoreThe subject of this research involves studying adsorption to removal herbicide Atlantis WG from aqueous solutions by bentonite clay. The equilibrium concentration have been determined spectra photometry by using UV-Vis spectrophotometer. The experimental equilibrium sorption data were analyzed by two widely, Langmuir and Freundlish isotherm models. The Langmuir model gave a better fit than Freundlich model The adsorption amount of (Atlantis WG) increased when the temperature and pH decreased. The thermodynamic parameters like ?G, ?H, and ?S have been calculated from the effect of temperature on adsorption process, is exothermic. The kinetic of adsorption process was studied depending on Lagergren ,Morris ? Weber and Rauschenberg equati
... Show MoreBackground: The primary stability of the dental implant is a crucial factor determining the ability to initiate temporary implant-supported prosthesis and for subsequent successful osseointegration, especially in the maxillary non-molar sites. This study assessed the reliability of the insertion torque of dental implants by relating it to the implant stability quotient values measured by the Osstell device. Material and methods: This study included healthy, non-smoker patients with no history of diabetes or other metabolic, or debilitating diseases that may affect bone healing, having non-restorable fractured teeth and retained roots in the maxillary non-molar sites. Primary dental implant stability was evaluated using a torque ratc
... Show MoreProblem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
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