Evaluation of Physicochemical Properties of Beetroot-Based Dietary Supplements

In the European Union, no specific requirements for the physicochemical parameters of dietary supplements have been established, contrary to the United States of America. This research aimed to assess the selected physical parameters of 31 commercially available beetroot-based dietary supplements in the form of tablets and capsules following the United States Pharmacopoeia (USP) guidelines and the Food and Drug Administration (FDA) recommendations. There was also estimated zinc and iron content by atomic absorption spectroscopy with flame detection. Results showed that nine products did not meet the USP requirements. Seven supplements needed more than 30 min to disintegrate. Two products in the form of tablets did not pass the friability test because of cracking. The hardness values varied significantly between manufacturers, demonstrating values from 59.1 to 455.8 N. The iron-enriched supplements differed significantly in iron content compared with the manufacturers’ declaration (84.91–140.69%). Inappropriate quality of dietary supplements, which may constitute a potential risk to consumers, can be related to the lack of specific regulations in Europe; hence, similar to the USA requirements should be considered in the European Union. The work emphasizes the need to better control the quality of dietary supplements before they are introduced to the European market.

Analysis of Flame Suppression Capabilities Using Low-Frequency Acoustic Waves and Frequency Sweeping Techniques

Symmetry plays a key role in the processing and analysis of not only visual but also acoustic signals in various multidisciplinary areas. New innovative and environmentally friendly methods for extinguishing flames are still being sought worldwide. One of these techniques appears to be the acoustic method. A laboratory stand was built for this purpose, which was coupled with the tested prototype of a high-power acoustic extinguisher, and then the original experiments and analyses of extinguishing effectiveness were carried out. For extinguishing, waveforms with specified parameters selected symmetrically around the frequency for which the extinguisher was designed were used. The aim of this article is to present and discuss selected measurement results concerning the possibility of flame extinguishing with the use of sinusoidal acoustic waves of low frequency (below 21 Hz), as well as with the use of frequency sweeping techniques with set parameters. Such an extinguisher can be equipped with an intelligent module so that the extinguisher may be activated automatically (without human intervention) when flames are detected. The benefits of this combination as well as the importance of image processing for flame detection are also presented in this paper. This solution, with its good fire detection and fast response, may be applicable for extinguishing firebreaks in particular.

Flame Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network

It is important for fire detectors to operate quickly in the event of a fire, but existing conventional fire detectors sometimes do not work properly or there are problems where non-fire or false reporting occurs frequently. Therefore, in this study, HSV color conversion and Harris Corner Detection were used in the image pre-processing step to reduce the incidence of false detections. In addition, among the detected corners, the vicinity of the corner point facing the upper direction was extracted as a region of interest (ROI), and the fire was determined using a convolutional neural network (CNN). These methods were designed to detect the appearance of flames based on top-pointing properties, which resulted in higher accuracy and higher precision than when input images were still used in conventional object detection algorithms. This also reduced the false detection rate for non-fires, enabling high-precision fire detection.

Flame Detection Based on Deep Learning Using HSV Color Model and Corner Detection Algorithm

Recently, many image classification or object detection models that use deep learning techniques have been studied; however, in an actual performance evaluation, flame detection using these models may achieve low accuracy. Therefore, the flame detection method proposed in this study is image pre-processing with HSV color model conversion and the Harris corner detection algorithm. The application of the Harris corner detection method, which filters the output from the HSV color model, allows the corners to be detected around the flame owing to the rough texture characteristics of the flame image. These characteristics allow for the detection of a region of interest where multiple corners occur, and finally classify the flame status using deep learning-based convolutional neural network models. The flame detection of the proposed model in this study showed an accuracy of 97.5% and a precision of 97%.

An Energy-segmented Moth-flame Optimization Algorithm for Function Optimization and Performance Measures Analysis

The moth-flame optimization algorithm (MFO) is a novel metaheuristic algorithm for simulating the lateral positioning and navigation mechanism of moths in nature, and it has been successfully applied to various optimization problems. This paper segments the flame energy of MFO by introducing the energy factor from the Harris hawks optimization algorithm, and different updating methods are adopted for moths with different flame-detection abilities to enhance the exploration ability of MFO. A new energy-segmented moth-flame optimization algorithm (ESMFO) is proposed and is applied on 21 benchmark functions and an engineering design problem. The experimental results show that the ESMFO yields very promising results due to its enhanced exploration, exploitation, and convergence capabilities, as well as its effective avoidance of local optima, and achieves better performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.