Separation of Trigonelline, the major alkaloid in fenugreek seeds, is difficult because the extract of these seeds usually contains Trigonelline, choline, mucilage, and steroidal saponins, in addition to some other substances. This study amis to isolate the quaternary ammonium alkaloid (Trigonelline) and choline from fenugreek seeds (Trigonella-foenum graecum L.) which have similar physiochemical properties by modifying of the classical method. Seeds were defatted and then extracted with methanol. The presence of alkaloids was detected by using Mayer's and Dragendorff's reagents. In this work, trigonilline was isolated with traces of choline by subsequent processes of purification using analytical and preparative TLC techniques. Further identification was done by using HPLC, IR and MP. Pure Trigonelline was isolated from the seeds of Trigonella-foenum graecum excluding other alkaloid like choline. In this study, a new, fast and convenient method for isolation and purification of Trigonelline from fenugreek seeds has been established. Unlike other methods, this one excludes all the non-alkaloidal components from the fenugreek seeds extract.
This research aims to assess the adoption of TQM in the Middle East Bank for Investment, and diagnosis means and techniques of technological innovation that applied in, as well as to determine the nature of the relationship between total quality management practices (operations management, employment relations, customer relations) and technological innovation (the incremental innovation of the service, incremental innovation process, a radical innovation of the service, a radical innovation of the operation), through use the checklists, derived from a study (Kim et al, 2012) the many styles of mathematical and statistical tools was adopted like the percentage, mean, duplicates, as well as the adoption of the Z test th
... Show MoreIn this paper, we investigate two stress-strength models (Bounded and Series) in systems reliability based on Generalized Inverse Rayleigh distribution. To obtain some estimates of shrinkage estimators, Bayesian methods under informative and non-informative assumptions are used. For comparison of the presented methods, Monte Carlo simulations based on the Mean squared Error criteria are applied.
In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H
... Show MoreBackground: Very low birth weight (VLBW) neonates constitute approximately 4–7 percent of all live births and their mortality is very high.
Objective: to find out if there is a relationship between Very Low Birth Weight Neonates and increased neonatal mortality for age 0 to 7 days.
Methods: A retrospective study of VLBW neonates admitted to NICU at Ibn Al- Baladi Pediatrics and Maternity hospital over one year (2012)were studied, The study period was from April till August 2013. Exclusion criteria were: (1) neonates weighing less than 700 g and with gestational age less than 24 weeks (abortion) (2) death in the delivery room (3) neonates weighing more than 1500 g. (4) Postnatal age more than 7 days. The outcome measure was in-hos
Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s
... Show MoreWellbore instability problems cause nonproductive time, especially during drilling operations in the shale formations. These problems include stuck pipe, caving, lost circulation, and the tight hole, requiring more time to treat and therefore additional costs. The extensive hole collapse problem is considered one of the main challenges experienced when drilling in the Zubair shale formation. In turn, it is caused by nonproductive time and increasing well drilling expenditure. In this study, geomechanical modeling was used to determine a suitable mud weight window to overpass these problems and improve drilling performance for well development. Three failure criteria, including Mohr–Coulomb, modifie
In the presence of deep submicron noise, providing reliable and energy‐efficient network on‐chip operation is becoming a challenging objective. In this study, the authors propose a hybrid automatic repeat request (HARQ)‐based coding scheme that simultaneously reduces the crosstalk induced bus delay and provides multi‐bit error protection while achieving high‐energy savings. This is achieved by calculating two‐dimensional parities and duplicating all the bits, which provide single error correction and six errors detection. The error correction reduces the performance degradation caused by retransmissions, which when combined with voltage swing reduction, due to its high error detection, high‐energy savings are achieved. The res
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