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Abstract Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Abstract The management of employees' salaries is an extremely complex task and time-consuming job due to having a large volume of payroll data and calculations. In many developing regions and countries including the Kurdistan Region of Iraq, the accounting processes of salary are manual. This leads to low speed in the calculation processes of deductions and allowances, easy making errors, difficulty to maintain salaries of previous months, low efficiency, and delay in generating reports. This paper represents the design, implementation, and evaluation of a web-based payroll management system (WPMS). This system can calculate the salary of every employee per month and annum efficiently and effectively. Moreover, it can keep the records of employees’ data including their pay, allowances, and deductions on monthly bases in the data mart. Additionally, the system can speedily and automatically generate employees’ payslips, accurate reports, and detailed statistics. Furthermore, WPMS provides a user-friendly environment and enables users to easily access, update, and delete data. The system was implemented by using programming languages: HTML, PHP, JavaScript, jQuery, AJAX, and MySQL. To conclude, the system was investigated for its usability by using the system usability scale tool. The results achieved an 87.8% score of usability satisfaction.

Ultrasound-assisted extraction (UAE) was applied to extract oil from blackberry (BB) seeds. The effect of UAE conditions on oil recovery and quality was investigated. Favorable experimental conditions (ultrasound intensity (UI), extraction temperature, and time) were investigated using response surface methodology (RSM). A Box-Behnken design was used to predict optimized conditions for BB seed oil extraction. These conditions were as follows: 13.77 W/cm 2 UI, 45 °C extraction temperature, and 15 min extraction time. The experimental value obtained for extraction efficiency under optimal conditions was 87 ± 0.34%, in good agreement with the optimized predicted value. UAE does not affect the oil composition and confers higher antioxidant values in BB seed oil in comparison with Soxhlet extraction.

The saponification value of fats and oils is one of the most common quality indices, reflecting the mean molecular weight of the constituting triacylglycerols. Proton nuclear magnetic resonance ( 1 H-NMR) spectra of fats and oils display specific resonances for the protons from the structural patterns of the triacylglycerols (i.e., the glycerol backbone), methylene (-CH 2 -) groups, double bonds (-CH=CH-) and the terminal methyl (-CH 3 ) group from the three fatty acyl chains. Consequently, chemometric equations based on the integral values of the 1 H-NMR resonances allow for the calculation of the mean molecular weight of triacylglycerol species, leading to the determination of the number of moles of triacylglycerol species per 1 g of fat and eventually to the calculation of the saponification value (SV), expressed as mg KOH/g of fat. The algorithm was verified on a series of binary mixtures of tributyrin (TB) and vegetable oils (i.e., soybean and rapeseed oils) in various ratios, ensuring a wide range of SV. Compared to the conventional technique for SV determination (ISO 3657:2013) based on titration, the obtained 1 H-NMR-based saponification values differed by a mean percent deviation of 3%, suggesting the new method is a convenient and rapid alternate approach. Moreover, compared to other reported methods of determining the SV from spectroscopic data, this method is not based on regression equations and, consequently, does not require calibration from a database, as the SV is computed directly and independently from the 1 H-NMR spectrum of a given oil/fat sample.

This paper investigated the limits of the current approach for the determination of the fatty acids profile of milk fats from proton nuclear magnetic resonance data based on the hypothesis that the signal at 0.96 ppm, currently assigned in the literature as a marker for the "short chain fatty acids," is generated only by the butyric moiety (not by all of the short-chain fatty acids, which also include C6:0-caproic acid). The hypothesis was tested and experimentally confirmed. Moreover, the triplet at 0.96 ppm can also be due to n-3 fatty acids such as linolenic acid (C18:3); therefore, a previously reported methodology for the fatty acids profiling of dairy products-considered as general in the literature-cannot be used in fraud-detection approaches because it allows linolenic acid to be mistaken for butyric acid, consequently leading to misclassification of adulterated samples as nonadulterated. To support our opinion, we have applied the current literature approach for the determination of the fatty acids composition of 3 synthetic nondairy fat blends and have obtained fatty acid compositions similar to milk fats, allowing for their misclassification as genuine milk fats. However, in reality, the blends had very different compositions, as confirmed by gas chromatography. Consequently, we have highlighted the weaknesses of the existing methodology for the detection of dairy food adulteration. In return, new proton nuclear magnetic resonance descriptors based on various integral ratios of signals associated with CH 2 moiety versus signals associated with butyric and n-3 fatty acids were proposed to detect adulterations., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

In the present study, we developed a method for the grape seed oil compositional characterisation using (1)H NMR spectroscopy directly applied on oils without sample derivatisation (as triglycerides). Using (1)H NMR spectroscopy data and systems of chemometric equations, we established the composition of grape seed oils on four classes of fatty acids. Spectral information from (1)H NMR and FT-IR spectroscopy was used to make the differences between grape seed oils and genuine common oils. Applying the PCA (Principal Component Analysis) method to the spectral information, it was evaluated the application potential in authenticity control of grape seed oils from common genuine oils (sunflower, soybean, linseed and rapeseed)., (Copyright © 2012 Elsevier Ltd. All rights reserved.)