Determination of benzimidazoles in pharmaceuticals and human serum by high-performance liquid chromatography

A liquid chromatographic method is described for the quantification of prednisolone, benzimidazoles, and preservatives using a C₁₈ analytical column as stationary phase. The mobile phase was 30:70 methanol:pH 2.5 phosphate buffer at a flow rate of 1.0 mL min^?1 with absorbance detection at 235 nm. The method was linear for concentrations ranged from 40–10,000 ng mL^?1. Low values of coefficient of variance were obtained when samples were analyzed as replicates. Excellent recovery values were recorded in commercial products and fortified samples. International Conference of Harmonization protocols were employed to perform comprehensive method validation. The reported method has applications for pharmaceutical and serum samples.     

A fuzzy multi-criteria decision making model for supplier selection

This paper presents a hybrid fuzzy model for group Multi Criteria Decision Making (MCDM). A modified fuzzy DEMATEL model is presented to deal with the influential relationship between the evaluation criteria. The modified DEMATEL captures such relationship and divides the criteria into two groups, particularly, the cause group and the effect group. The cause group has an influence on the effect group where such influence is used to estimate the criteria weights. In addition, a modified TOPSIS model is proposed to evaluate the criteria against each alternative. Here, a fuzzy distance measure is used in which the distance from the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) are calculated. The resulted distances were used to calculate the similarity to Ideal and Anti-ideal points. Later, an optimal membership degree (closeness coefficient) of each alternative is computed to estimate to which extent an alternative belongs to both FPIS and FNIS. The closer the degree of membership to FPIS and the farther from FNIS the more preferred the alternative. The membership degree is obtained by the optimization of a defined objective function that measures the degree to which an alternative is similar/dissimilar to the Ideal/Anti-Ideal solutions. The closeness coefficient is used to rank the alternatives. To better have a high contrast between the ranks of alternatives an optimization problem was introduced and solved to maximize the contrast.The presented hybrid model was applied on an industrial case study for the selection of cans supplier/suppliers at Nutridar Factory in Amman-Jordan to demonstrate the proposed model. Finally a sensitivity analysis is introduced to verify the resulting ranks of the available suppliers via testing different values of the used parameters. The sensitivity analysis has shown robust and valid results that are close to real preferences of the consulted experts.     

Hypermedia annotation presentation: The effect of location and type on the EFL learners’ achievement in reading comprehension and vocabulary acquisition

This study investigated the effect of gloss presentation in different text locations whilst participants read EFL texts in a hypermedia environment. Seventy-eight undergraduate EFL learners read and summarized seven texts and completed a vocabulary assessment. The number of propositions recalled in each summary was recorded. The data suggest that reading passages with hypermedia annotations significantly benefits passage comprehension and vocabulary (compared to reading passages with no annotations). The best performance was observed in the condition where glosses were placed after the glossed word. The study also reported large observed score mean differences for the definition gloss type of 3–5-words.     

Reactivity feedbacks of a material test research reactor fueled with various low enriched uranium dispersion fuels

The reactivity feedbacks of a material test research reactor using various low enriched uranium fuels, having same uranium density were calculated. For this purpose, the original aluminide fuel (UAl_x–Al) containing 4.40 gU/cm³ of an MTR was replaced with silicide (U₃Si–Al and U₃Si₂–Al) and oxide (U₃O₈–Al) dispersion fuels having the same uranium density as of the original fuel. Calculations were carried out to find the fuel temperature reactivity feedback, moderator temperature reactivity feedback, moderator density reactivity feedback and moderator void reactivity feedback. Nuclear reactor analysis codes including WIMS-D4 and CITATION were employed to carry out these calculations. It was observed that the magnitudes all the respective reactivity feedbacks from 38 °C to 50 °C and 100 °C, at the beginning of life, of all the fuels were very close to each other. The fuel temperature reactivity feedback of the U₃O₈–Al was about 2% more than the original UAl_x–Al fuel. The magnitudes of the moderator temperature, moderator density and moderator void reactivity feedbacks of all the fuels, showed very minor variations from the original aluminide fuel.     

Performance of Anaerobic Reactors during Pseudo-Steady-State Operation

Four anaerobic reactors were studied for the purposes of this work: two anaerobic fluidized bed reactors (AFBR) using diatomaceous earth and granular activated carbon as immobilization media (R1 and R2, respectively), a packed-bed reactor (R3), and a suspended growth reactor (R4). A nutrient-supplemented wastestream with glucose as the main carbon source was treated. Successful reactor start-up was achieved for all four anaerobic reactors. These reactors were able to handle organic loading rates of more than 12000, 7500, 6000 and 650 mg dm⁻³ day⁻¹ for R1–R4, respectively. Anaerobic fluidized bed reactors were less affected by interruptions and adverse operating conditions than were packed-bed and suspended growth reactors. Immobilized cell reactors and, specifically, AFBRs were clearly superior to conventional high-rate digesters. This enhanced performance is primarily due to the very high cell retention ability of such reactors. High total organic carbon (TOC) removal efficiencies were achievable under pseudo-steady state operation. Removal efficiencies above 98% were observed for all reactors. Specific biogas production rates of 1·5–1·7, 1·4–1·7, 1·1–1·5 and 0·9–1·3 dm³ of methane per gram of TOC removed for R1–R4, respectively, were attained. A consistent biogas methane content of 52·5–55·9% was observed. Biomass concentrations of 84, 91, 21 and 1·9 g VS dm⁻³ were measured for R1–R4, respectively. Extremely high biomass concentrations in AFBRs were possible due to the high available specific surface area. © 1997 SCI.     

Determination of traces of heavy metals in oils and fats by arc spectrography

A new and simple method is described for direct and simultaneous determination of traces in fats and oils of iron, copper, nickel, lead, silver, aluminum, magnesium, calcium and manganese. Graphite powder is used as spectroscopic buffer and for preparation of standard powders. Using necked cupped carbon electrode and cone shaped counter electrode, a steady DC arc discharge is obtained with graphite matrix. A quartz spectrograph of high resolving power is used for this determination.     

Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

Novel Coronavirus-19 (COVID-19) is a newer type of coronavirus that has not been formally detected in humans. It is established that this disease often affects people of different age groups, particularly those with body disorders, blood pressure, diabetes, heart problems, or weakened immune systems. The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates. Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans. It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken. The latest global coronavirus epidemic (COVID-19) has brought new challenges to the scientific community. Artificial Intelligence (AI)-motivated methodologies may be useful in predicting the conditions, consequences, and implications of such an outbreak. These forecasts may help to monitor and prevent the spread of these outbreaks. This article proposes a predictive framework incorporating Support Vector Machines (SVM) in the forecasting of a potential outbreak of COVID-19. The findings indicate that the suggested system outperforms cutting-edge approaches. The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance. The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity. The proposed SVM system model exhibits 98.88% and 96.79% result in terms of accuracy during training and validation respectively.     

Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts; consequently, stakeholders are interested in more updates of their capabilities. Code smells arise as one of the obstacles in the software industry. They are characteristics of software source code that indicate a deeper problem in design. These smells appear not only in the design but also in software implementation. Code smells introduce bugs, affect software maintainability, and lead to higher maintenance costs. Uncovering code smells can be formulated as an optimization problem of finding the best detection rules. Although researchers have recommended different techniques to improve the accuracy of code smell detection, these methods are still unstable and need to be improved. Previous research has sought only to discover a few at a time (three or five types) and did not set rules for detecting their types. Our research improves code smell detection by applying a search-based technique; we use the Whale Optimization Algorithm as a classifier to find ideal detection rules. Applying this algorithm, the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the within-class variance. The proposed framework adopts if-then detection rules during the software development life cycle. Those rules identify the types for both medium and large projects. Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes. The proposed detection framework has an average of 94.24% precision and 93.4% recall. These accurate values are better than other search-based algorithms of the same field. The proposed framework improves code smell detection, which increases software quality while minimizing maintenance effort, time, and cost. Additionally, the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.