Production of Novel Bigels from Cold Pressed Chia Seed Oil By-Product: Application in Low-Fat Mayonnaise.

The objective of this study was to produce an innovative bigel formulation by combining glycerol monostearate (GMS) oleogel with hydrogels stabilized by various agents, including cold pressed chia seed oil by-product gum (CSG), gelatin (G), and whey protein concentrate (WPC). The findings indicated that the choice of hydrogel influenced the rheological, textural, and microstructural properties of the bigels. The G' value of the bigel samples was higher than G″, indicating that all the bigels exhibited solid-like characteristics. In order to numerically compare the dynamic rheological properties of the samples, K' and K″ values were calculated using the power law model. K' values of the samples were found to be higher than K″ values. The K' value of bigel samples was significantly affected by the hydrogel (HG)/oleogel ratio (OG) and the type of stabilizing agent used in the hydrogel formulation. As the OG ratio of bigel samples increased, the K' value increased significantly (p < 0.05). The texture values of the samples were significantly affected by the HG/OG ratio (p < 0.05). The study's findings demonstrated that utilizing CSG, G, and WPC at an OG ratio more than 50% can result in bigels with the appropriate hardness and solid character. The low-fat mayonnaise was produced by using these bigels. The low-fat mayonnaise showed shear-thinning and solid-like behavior with G' values greater than the G″ values. Low-fat mayonnaise produced with CSG bigels (CSGBs) showed similar rheological properties to the full-fat mayonnaise. The results showed that CSG could be used in a bigel formulation as a plant-based gum and CSGB could be used as a fat replacer in low-fat mayonnaise formulation.

Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods.

Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient's age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches.

The G20 countries are the locomotives of economic growth, representing 64% of the global population and including 4.7 billion inhabitants. As a monetary and market value index, real gross domestic product (GDP) is affected by several factors and reflects the economic development of countries. This study aimed to reveal the hidden economic patterns of G20 countries, study the complexity of related economic factors, and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019 (COVID-19) pandemic recession (2019-2020). In this respect, this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita, industrial production, government spending, COVID-19 cases/population, patient recovery, COVID-19 death cases, number of hospital beds/1000 people, and percentage of the vaccinated population to identify clusters for G20 countries. The clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic development. The results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries, splitting them into three clusters by sharing different measurements and patterns (harmonies and variances across G20 countries). A comprehensive statistical analysis was performed to analyze endogenous and exogenous factors. Similarly, the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic recession. Variables such as GDP per capita and patient recovery of COVID-19 cases with values of $12,012 and 82.8%, respectively, were the most significant factors for clustering the G20 countries, with a correlation coefficient (R2) of 91.8%. The results and findings offer some crucial recommendations to handle pandemics in terms of the suggested economic systems by identifying the challenges that the G20 countries have experienced.

An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms.

Design and implementation of biological neural networks is a vital research field in the neuromorphic engineering. This paper presents LUT-based modeling of the Adaptive Exponential integrate-and-fire (ADEX) model using Nyquist frequency method. In this approach, a continuous term is converted to a discrete term by sampling factor. This new modeling is called N-LUT-ADEX (Nyquist-Look Up Table-ADEX) and is based on accurate sampling of the original ADEX model. Since in this modeling, the high-accuracy matching is achieved, it can exactly reproduce the spiking patterns, which have the same behaviors of the original neuron model. To confirm the N-LUT-ADEX neuron, the proposed model is realized on Virtex-II Field-Programmable Gate Array (FPGA) board for validating the final hardware. Hardware implementation results show the high degree of similarity between the proposed and original models. Furthermore, low-cost and high-speed attributes of our proposed neuron model will be validated. Indeed, the proposed model is capable of reproducing the spiking patterns in terms of low overhead costs and higher frequencies in comparison with the original one. The properties of the proposed model cause can make it a suitable choice for neuromorphic network implementations with reduced-cost attributes.

Effectiveness of using E-learning systems during COVID-19 in Saudi Arabia: Experiences and perceptions analysis of engineering students.

The COVID-19 epidemic has affected most countries across the globe since it was declared in December 2019 and forced most educational institutions to shift from face-to-face learning style to E-learning or distance education. This study aims to analyze and investigate the experiences and perceptions of using Blackboard as a distance learning (online) platform. The study was conducted in one of the top universities in Saudi Arabia and the Middle east. A survey-based study is constructed and distributed among undergraduate students including males, and females in an engineering college. Two hundred thirty-five students participated in this study; males represent (74%) and (26%) for females. Ten phases containing 38 items of advantages and disadvantages are considered in the survey study to understand the advantages, constraints, and difficulties of the Blackboard. Two nonparametric statistical tools of Mann-Whitney and Kruskal-Wallis are used for analyzing the survey. The study shows significant results regarding difference perceptions on Blackboard between gender and engineering disciplines. The results of this study can help the educational decision-makers in the ministry of education and universities improve the quality and increase the sustainability of the EL resources. Moreover, the findings reveal that males, females, and engineering disciplines have different perceptions towards the use of virtual learning.

Correction: Wang et al. An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms. Biology 2022, 11, 1125.

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