This study investigated the survival of encapsulated potential probiotic Lactobacillus plantarum which isolated from fermented cocoa beans. κ-Carrageenan was used to encapsulate the probiotic. Encapsulation techniques such as emulsification, freeze-drying or extrusion were adopted to encapsulate the probiotic. Freeze-drying and extrusion methods showed higher (p
H2S gas when exposed to metal can be responsible for both general and localized corrosion, which depend on several parameters such as H2S concentration and the corrosion product layer formed. Therefore, the formation of passive film on 316L steel when exposed to H2S environment was investigated using several analysis methods such as FESEM and STEM/EDS analyses, which identified a sulfur species underneath the porous structure of the passive film. X-ray photoelectron spectroscopy analysis demonstrated that the first layer of CrO3 and Cr2O3 was dissolved, accelerated by the presence of H2S-Cl-. An FeS2 layer was formed by incorporation of Fe and sulfide; then, passivation by Mo took place by forming a MoO2 layer. NiO, Ni(OH)2, and NiS barriers are formed as final protection for 316L steel. Therefore, Ni and Mo play an important role as a dual barrier to maintain the stability of 316L steel in high pH2S environments. For safety concern, this paper is aimed to point out a few challenges dealing with high partial pressure of H2S and limitation of 316L steel under highly sour condition for the oil and gas production system.
The goal of this study was to determine inhibitory effect of palm kernel expeller (PKE) peptides of different degree of hydrolysis (DH %) against spore-forming bacteria Bacillus cereus, Bacillus circulans, Bacillus coagulans, Bacillus licheniformis, Bacillus megaterium, Bacillus pumilus, Bacillus stearothermophillus, Bacillus subtilis, Bacillus thuringiensis, Clostridium perfringens; and non-spore-forming bacteria Escherichia coli, Lisinibacillus sphaericus, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella Typhimurium and Staphylococcus aureus.
The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.
The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.
COVID-19 (coronavirus disease 2019) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2. Recently, it has been demonstrated that the voice data of the respiratory system (i.e., speech, sneezing, coughing, and breathing) can be processed via machine learning (ML) algorithms to detect respiratory system diseases, including COVID-19. Consequently, many researchers have applied various ML algorithms to detect COVID-19 by using voice data from the respiratory system. However, most of the recent COVID-19 detection systems have worked on a limited dataset. In other words, the systems utilize cough and breath voices only and ignore the voices of the other respiratory system, such as speech and vowels. In addition, another issue that should be considered in COVID-19 detection systems is the classification accuracy of the algorithm. The particle swarm optimization-extreme learning machine (PSO-ELM) is an ML algorithm that can be considered an accurate and fast algorithm in the process of classification. Therefore, this study proposes a COVID-19 detection system by utilizing the PSO-ELM as a classifier and mel frequency cepstral coefficients (MFCCs) for feature extraction. In this study, respiratory system voice samples were taken from the Corona Hack Respiratory Sound Dataset (CHRSD). The proposed system involves thirteen different scenarios: breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowels. The experimental results demonstrated that the PSO-ELM was capable of attaining the highest accuracy, reaching 95.83%, 91.67%, 89.13%, 96.43%, 92.86%, 88.89%, 96.15%, 96.43%, 88.46%, 96.15%, 96.15%, 95.83%, and 82.89% for breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowel scenarios, respectively. The PSO-ELM is an efficient technique for the detection of COVID-19 utilizing voice data from the respiratory system.
In this study, sago starch was modified in order to enhance its physicochemical properties. Carboxymethylation was used to introduce a carboxymethyl group into a starch compound. The carboxymethyl sago starch (CMSS) was used to prepare smart hydrogel by adding acetic acid into the CMSS powder as the crosslinking agent. The degree of substitution of the CMSS obtained was 0.6410. The optimization was based on the gel content and degree of swelling of the hydrogel. In this research, four parameters were studied in order to optimize the formation of CMSS-acid hydrogel. The parameters were; CMSS concentration, acetic acid concentration, reaction time and reaction temperature. From the data analyzed, 76.69% of optimum gel content was obtained with 33.77 g/g of degree of swelling. Other than that, the swelling properties of CMSS-acid hydrogel in different media such as salt solution, different pH of phosphate buffer saline solution as well as acidic and alkaline solution were also investigated. The results showed that the CMSS-acid hydrogel swelled in both alkaline and salt solution, while in acidic or low pH solution, it tended to shrink and deswell. The production of the hydrogel as a smart material offers a lot of auspicious benefits in the future especially related to swelling behaviour and properties of the hydrogel in different types of media.
The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.
The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.
Hypercholesterolemia is one of the most common chronic diseases in human. Along with chemical therapy traditional medication is used as hypocholesterolemic remedy, however, with unfavorable side effects. Recently, Monascus fermented product (MFP) has become a popular hypocholesterolemic natural supplement. In the present study, the hypocholesterolemic activity of Monascus purpureus FTC5391 fermented product ethanolic extract (MFPe) was investigated in hypercholesterolemic rats. Results showed that MFPe not only reduced the serum total cholesterol (TC), LDL-C, TG concentration, and TC/HDL-C ratio but also increased the HDL-C. Further, solid phase extraction (SPE) was carried out to obtain the hypocholesterolemic bioactive fraction. The high polar fraction of SPE increased the HDL-C (42%) and decreased the TC (53.3%), LDL-C (47%), and TG (50.7%) levels as well as TC/HDL-C ratio (69.1%) in serum. The GC-MS results of the active fraction revealed two main compounds, isosorbide and erythritol, which act as coronary vasodilator compounds.