This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Two green, simple, and accurate chromatographic methods were developed and validated for the simultaneous determination of omeprazole and aspirin mixture in the presence of salicylic acid, a major impurity of aspirin. Method A is a reversed‐phase ultra‐high‐performance liquid chromatography; the separation was performed on a C18 column, with a mobile phase composed of ethanol:0.1% aqueous solution of triethylamine acidified with orthophosphoric acid (pH 3) (30:70, v/v) at 0.15 mL/min flow rate and 230 nm. Omeprazole, aspirin, and aspirin impurity retention times were 7.47, 4.40, and 5.13 min, respectively. Good linearity was achieved in the concentration ranges of 5–80, 5–85, and 3–50 μg/mL for the three mentioned components, respectively. Method B is thin‐layer chromatography (TLC) where silica gel TLC F254 plates were utilized to achieve separation using ethanol:ethyl acetate (2:8, v/v) as a developing system at 240 nm. The resulted Rf values were 0.83, 0.65, and 0.23 for omeprazole, aspirin, and impurity, respectively. The concentration ranges of 0.1–3 μg/band for the three drugs showed good linearity. The proposed methods are eco‐friendly and greener when compared to the already reported method (Microchemical Journal, 152, 104350). This is the first use of TLC method for the determination of the three drugs. International Council for Harmonization (ICH) guidelines were followed to ensure the validity of developed methods.

Background Sickle cell disease (SCD) is an inherited hematological disorder where the shape of red blood cells is altered, resulting in the destruction of red blood cells, anemia, and other complications. SCD is prevalent in the southern and eastern provinces of the Arabian peninsula. The most common complications for individuals with SCD are acute painful episodes that require several doses of intravenous opioids, making pain control for these individuals challenging. Instead of opioids, some studies have suggested that ketamine might be used for pain control in acute pain episodes of individuals with SCD. This study aims to evaluate whether the addition of ketamine to morphine can achieve better pain control, decreasing the number of repeated doses of opiates. We hypothesize that early administration of ketamine would lead to a more rapid improvement in pain score and lower opioid requirements. Methods and analysis This study will be a prospective, randomized, concealed, blinded, pragmatic parallel group, controlled trial enrolling adult patients with SCD and acute vaso-occlusive crisis pain. All patients will receive standard analgesic therapy during evaluation. Patients randomized to the treatment arm will receive low-dose ketamine (0.3 mg/kg in 0.9% sodium chloride, 100 ml bag) in addition to standard intravenous hydration, while those in the control group will receive a standard dose of morphine (0.1 mg/kg in 0.9% sodium chloride, 100 ml bag) in addition to the standard intravenous hydration. All healthcare providers will be blinded to the treatment arm. Data will be analyzed according to the intention-to-treat principle. The primary outcome is improvement in pain severity using the Numerical Pain Rating Score. Trial registration Clinicaltrials.gov, NCT03431285 . Registered on 13 February 2018 Electronic supplementary material The online version of this article (10.1186/s13063-019-3394-4) contains supplementary material, which is available to authorized users.

Automatic optical inspection (AOI) is a control process for precisely evaluating the completeness and quality of manufactured products with the help of visual information. Automatic optical inspection systems include cameras, light sources, and objects; AOI requires expert operators and time-consuming setup processes. In this study, a novel autonomous industrial robot-guided inspection system was hypothesized and developed to expedite and ease inspection process development. The developed platform is an intuitive and interactive system that does not require a physical object to test or an industrial robot; this allows nonexpert operators to perform object inspection planning by only using scanned data. The proposed system comprises an offline programming (OLP) platform and three-dimensional/two-dimensional (3D/2D) vision module. A robot program generated from the OLP platform is mapped to an industrial manipulator to scan a 3D point-cloud model of an object by using a laser triangulation sensor. After a reconstructed 3D model is aligned with a computer-aided design model on a common coordinate system, the OLP platform allows users to efficiently fine-tune the required inspection positions on the basis of the rendered images. The arranged inspection positions can be directed to an industrial manipulator on a production line to capture real images by using the corresponding 2D camera/lens setup for AOI tasks. This innovative system can be implemented in smart factories, which are easily manageable from multiple locations. Workers can save scanned data when new inspection positions are included based on cloud data. The present system provides a new direction to cloud-based manufacturing industries and maximizes the flexibility and efficiency of the AOI setup process to increase productivity.

Visual inspection is an important task in manufacturing industries in order to evaluate the completeness and quality of manufactured products. An autonomous robot-guided inspection system was recently developed based on an offline programming (OLP) and RGB-D model system. This system allows a non-expert automatic optical inspection (AOI) engineer to easily perform inspections using scanned data. However, if there is a positioning error due to displacement or rotation of the object, this system cannot be used on a production line. In this study, we developed an automated position correction module to locate an object’s position and correct the robot’s pose and position based on the detected error values in terms of displacement or rotation. The proposed module comprised an automatic hand–eye calibration and the PnP algorithm. The automatic hand–eye calibration was performed using a calibration board to reduce manual error. After calibration, the PnP algorithm calculates the object position error using artificial marker images and compensates for the error to a new object on the production line. The position correction module then automatically maps the defined AOI target positions onto a new object, unless the target position changes. We performed experiments that showed that the robot-guided inspection system with the position correction module effectively performed the desired task. This smart innovative system provides a novel advancement by automating the AOI process on a production line to increase productivity.

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