In this paper, turbidimetric and reversed-phase ultra-fast liquid chromatography (UFLC) methods were described for the quantitative determination of ephedrine hydrochloride in pharmaceutical injections form. The first method is based on measuring the turbidimetric values for the formed yellowish white precipitate in suspension status in order to determine the ephedrine hydrochloride concentration. The suspended substance is formed as a result of the reaction of ephedrine hydrochloride with phosphomolybdic acid which was used as a reagent. The physical and chemical characteristics of the complex were investigated. The calibration graphs of ephedrine were established by turbidity method. While the second method (UFLC) was conducted using the methanol-water (55+45, v/v) as the mobile phase with adjusted water pH 3.5. The ephedrine hydrochloride was detected and measured using UV detector at 260 nm. The linearity of ephedrine was obtained in the range of 0.09–0.39 mmol·l-1 . The detection limits (LOD) for the ephedrine hydrochloride were found to be 0.4 and 0.0044 mmol·l-1 by turbidity and UFLC, respectively. The developed methods were successfully applied for the quantitative determination of ephedrine hydrochloride in laboratory preparations (standard) and in commercial pharmaceutical injections. The two methods have given relative standard deviations (R.S.D.) in the range of 0.65–1.69 %, which indicates reasonable repeatability and high precision of both methods.
A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.
Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments ach
... Show MoreDeveloping smart city planning requires integrating various techniques, including geospatial techniques, building information models (BIM), information and communication technology (ICT), and artificial intelligence, for instance, three-dimensional (3D) building models, in enabling smart city applications. This study aims to comprehensively analyze the role and significance of geospatial techniques in smart city planning and implementation. The literature review encompasses (74) studies from diverse databases, examining relevant solutions and prototypes related to smart city planning. The focus highlights the requirements and preparation of geospatial techniques to support the transition to a smart city. The paper explores various aspects,
... Show MoreBackground: One way to target polypharmacy and inappropriate medication in hemodialysis (HD) patients is with medication deprescribing. Objective: To assess the impact of implementing a pharmacist-led deprescribing program on medication adherence among HD patients. Method: A prospective interventional, one-group pretest-posttest-only design study was conducted at a hemodialysis center in Wasit Governorate, Iraq. Medication reconciliation followed by medication review based on the deprescribing program was done for all eligible patients, and the patients were monitored for three months for any possible complications. Results: Two hundred and seventy patients were screened for eligibility. Only one hundred and eighteen were enrolled i
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