Effects of Collinearity on Combining Neural Networks

Collinearity or linear dependency among a number of estimators may pose a serious problem when combining these estimators. The corresponding outputs of a number of neural networks NNs , which are trained to approximate the same quantity or quantities , may be highly correlated. Thus, the estimation of the optimal weights for combining such networks may be subjected to the harmful effects of collinearity, which results in a final model with inferior generalizations ability compared with the individual networks. In this paper, we investigate the harmful effects of collinearity on the estimation of the optimal weights for combining a number on NNs. We discuss an approach for selecting the component networks in order to improve the generalization ability of the combined model. Our experimental results demonstrate significant improvements in the generalization ability of a combined model as a result of the proper selection of the component networks. The approximation accuracy of the combined model is compared with two common alternatives: the apparent best network or the simple average of the corresponding outputs of the networks.     

Characterization of aerodynamic performance of wind-lens turbine using high-fidelity CFD simulations

Wind-lens turbines (WLTs) exhibit the prospect of a higher output power and more suitability for urban areas in comparison to bare wind turbines. The wind-lens typically comprises a diffuser shroud coupled with a flange appended to the exit periphery of the shroud. Wind-lenses can boost the velocity of the incoming wind through the turbine rotor owing to the creation of a low-pressure zone downstream the flanged diffuser. In this paper, the aerodynamic performance of the wind-lens is computationally assessed using high-fidelity transient CFD simulations for shrouds with different profiles, aiming to assess the effect of change of some design parameters such as length, area ratio and flange height of the diffuser shroud on the power augmentation. The power coefficient (C_p) is calculated by solving the URANS equations with the aid of the SST k–ω model. Furthermore, comparisons with experimental data for validation are accomplished to prove that the proposed methodology could be able to precisely predict the aerodynamic behavior of the wind-lens turbine. The results affirm that wind-lens with cycloidal profile yield an augmentation of about 58% increase in power coefficient compared to bare wind turbine of the same rotor swept-area. It is also emphasized that diffusers (cycloid type) of small length could achieve a twice increase in power coefficient while maintaining large flange heights.     

Improving the textural properties of Egyptian kariesh cheese by addition of hydrocolloids

In this study, the effect of addition of hydrocolloids (commercial pectin, citrus pectin or carboxymethylcellulose) to Egyptian Kariesh cheese at 0.2, 0.4 and 0.6% (w/w) on chemical composition, yield, rheological and sensory characteristics was investigated. The results revealed that the addition of hydrocolloids increased moisture content and yield values of Kariesh cheeses compared with control. Protein, salt and ash content decreased with increasing hydrocolloids levels. Rheological characteristics (hardness, adhesiveness, springiness, cohesiveness, gumminess, chewiness, resilience) were significantly lower in cheeses made with hydrocolloids. Kariesh cheese made with 0.4% w/w commercial pectin and 0.6% w/w carboxymethylcellulose recorded the highest scores for sensory attributes.     

Identification of <i>PtGai</i> (a DELLA protein) in trifoliate orange and expression patterns in response to drought stress

Gibberellins (GAs) are an important hormone in regulating plant growth and development, and DELLA protein is an essential negative regulator of GA signal transduction. The aim of the study was to clone a GA-inhibiting protein DELLA from trifoliate orange (Poncirus trifoliata L. Raf.) and to analyze the bioinformations and expression patterns of the protein gene in tissues and in response to drought stress. A DELLA protein was isolated from trifoliate orange and named as PtGai (Genebank number: MZ170959). The PtGai protein had 1731 bp open reading frames, along with 576 amino acid codes, and also grouped with sweet orange (XM_006430552.4). The PtGai protein sequence was 65% homology with the sequences of DELLA proteins in other plant families. PtGai protein existed in the nucleus based on the prediction of subcellular localization. PtGai protein could be expressed in roots, stems, and leaves, along with the highest expression in stems. PtGai was upregulated by drought stress in leaves and roots, along with the decrease of root total GA concentration and the inhibition of shoot and root biomass production. It indicated the characteristics of PtGai protein and the roles of PtGai in GA synthesis and plant growth.     

Biosynthesis of raw starch degrading β-cyclodextrin glycosyltransferase by immobilized cells of <i>Bacillus licheniformis</i> using potato wastewater

The study was sought to enhance the synthesis of thermal stable β-cyclodextrin glycosyltransferase (β-CGTase) using potato wastewater as a low-cost medium and assess the degree to which it is efficient for industrial production of β-cyclodextrin (β-CD) from raw potato starch. Thermophilic bacteria producing β-CGTase was isolated from Saudi Arabia and the promising strain was identified as Bacillus licheniformis using phylogenetic analysis of the 16S rRNA gene. Alginate-encapsulated cultures exhibited twice-fold of β-CGTase production more than free cells. Scanning electron microscopy (SEM) of polymeric capsules indicated the potential for a longer shelf-life, which promotes the restoration of activity in bacterial cells across semi-continuous fermentation of β-CGTase production for 252 h. The optimal conditions for β-CGTase synthesis using potato wastewater medium were at 36 h, pH of 8.0, and 50°C with 0.4% potato starch and 0.6% yeast extract as carbon and nitrogen sources, respectively. The purified enzyme showed a specific activity of 63.90 U/mg with a molecular weight of ∼84.6 kDa as determined by SDS-PAGE analysis. The high enzyme activity was observed up to 60°C, and complete stability was achieved at 75°C. High levels of activity and stability were shown at pH 8.0, and the pH range from 7.0–10.0, respectively. The enzyme has an appreciable affinity for raw potato starch with a Km of 5.7 × 10⁻⁶ M and a Vmax of 87.71 µmoL/mL/min. β-CD production was effective against 25 U/g of raw potato starch. The outcomes demonstrated its feasibility to develop a fermentation process by integrating the cost-effective production of β-CGTase having distinctive properties for β-CD production with ecofriendly utilization of potato wastewater.     

Secondary antiviral metabolites from fungi with special reference to coronaviruses

Profound inspection of the life forms on the earth teaches how to be the complexity of interrelationships among the various systems. Because of the emergence of novel viruses all the time and the inadequate of vaccines and antivirals, viral contagions are amongst the most causative diseases affecting people worldwide. Fungi exemplify a massive source of bioactive molecules as, many fungal secondary metabolities like Oxoglyantrypine, Carneic acid F, Scedapin C, Asteltoxin E, Phomanolide, Norquinadoline A and Quinadoline B have antiviral activity. This review deals with how secondary metabolites of fungi can help in the war against viruses in general and especially Coronaviruses moreover several pieces of literature pointed out that many clusters of fungi in different biotopes are waiting to be exploited.