Prediction of Egg Freshness and Albumen Quality Using Visible/Near Infrared Spectroscopy

Important changes occur in egg during storage leading to loss of quality. Prediction of these changes is critical in order to monitor egg quality and freshness. The aim of this research was to evaluate application of visible (VIS) and near infrared (NIR) spectroscopy as a rapid and non-destructive technique for egg quality assessment. Three hundred and sixty intact white-shelled eggs freshly laid by the same flock of hens fed with a standard feed were obtained. They were put under controlled conditions of temperature and humidity (T = 18 °C and RH = 55%) for 16 days of storage. Forty eggs were analyzed at day 0, 2, 4, 6, 8, 10, 12, 14, and 16. Transmission spectral data was obtained in the range from 350 to 2,500 nm. The non-destructive spectral data was compared to egg sample’s Haugh unit (HU) and albumen pH in terms of quality and to the number of storage days in terms of freshness. A partial least squares predictive model was developed and used to link the destructive assessment methods and the number of storage days with the spectral data. The correlation coefficient between the measured and predicted values of HU, albumen pH, and number of storage days were up to 0.94, R ² was up to 0.90 and the root mean square error values for the validation were 5.05, 0.06, and 1.65, respectively. These results showed that VIS/NIR transmission spectroscopy is a good tool for assessment of egg freshness and albumen pH and can be used as a non-destructive method for the prediction of HU, albumen pH, and number of storage days. In addition, the relevant information about these parameters was in the VIS and NIR ranging from 411 to 1,729 nm.     

Effects of Plant Growth Hormones on Mucor indicus Growth and Chitosan and Ethanol Production

The objective of this study was to investigate the effects of indole-3-acetic acid (IAA) and kinetin (KIN) on Mucor indicus growth, cell wall composition, and ethanol production. A semi-synthetic medium, supplemented with 0–5 mg/L hormones, was used for the cultivations (at 32 °C for 48 h). By addition of 1 mg/L of each hormone, the biomass and ethanol yields were increased and decreased, respectively. At higher levels, however, an inverse trend was observed. The glucosamine fraction of the cell wall, as a representative for chitosan, followed similar but sharper changes, compared to the biomass. The highest level was 221% higher than that obtained without hormones. The sum of glucosamine and N-acetyl glucosamine (chitin and chitosan) was noticeably enhanced in the presence of the hormones. Increase of chitosan was accompanied by a decrease in the phosphate content, with the lowest phosphate (0.01 g/g cell wall) being obtained when the chitosan was at the maximum (0.45 g/g cell wall). In conclusion, IAA and KIN significantly enhanced the M. indicus growth and chitosan production, while at the same time decreasing the ethanol yield to some extent. This study shows that plant growth hormones have a high potential for the improvement of fungal chitosan production by M. indicus.     

Two-level distributed clustering routing algorithm based on unequal clusters for large-scale Internet of Things networks

The Journal of Supercomputing - According to the recent advancements in communication technologies and the widespread use of smart devices, our environment can be transforming into the Internet of...     

Real-time monitoring of events applied to syndromic surveillance

Abstract

This article focuses on monitor plans aimed at the early detection of the increase in the frequency of events. The literature recommends either monitoring the time between events (TBE) if events are rare or counting the number of events per unit non-overlapping time intervals otherwise. Some authors advocate using the Bernoulli model for rare events, applying presence or absence of events within non-overlapping and exhaustive time intervals. This Bernoulli model does improve the real-time monitoring assessment of these events compared to counting events over a larger interval, making them less rare. However this approach became inefficient if more than one event starts occurring within the intervals. Monitoring TBE is the real-time option for outbreak detection, because outbreak information is accumulated when an event occurs. This is preferred to waiting for the end of a period to count events. If the TBE reduces significantly, then the incidence of these events increases significantly. This article explores this TBE option relative to using the monitoring of counts when the TBEs are either Exponentially, Gamma or Weibull distributed for moderately low count scenarios. The article will discuss and compare the approaches of using an Exponentially Weighted Moving Average (EWMA) statistic for the TBEs to the EWMA of counts. Several robust options will be considered when the future change in event frequency is unknown. Our goal is to have a robust monitoring plan which is able to efficiently detect many different levels of shifts. These robust plans are compared to the more traditional event monitoring plans for both small and large changes in the event frequency.     

Vicious cycle during chemical degradation of sulfonated aromatic proton exchange membranes in the fuel cell application

Weak phase separation and vulnerable linking groups between aromatic units are common setbacks of sulfonated aromatic proton exchange membranes (PEMs) from durability point of view. In this study, sulfonated poly(ether ether ketone) (SPEEK) membranes were exposed to Fenton's solution for a specific time, ranging from 10 to 60 minutes. Chemical structure and morphology evolution, decay in mechanical and thermal stability, and H₂ permeability of SPEEK membranes were evaluated during the chemical degradation. Less-entangled polymeric chains with lower average molecular weight of degraded SPEEK samples diminished mechanical rigidity. In addition, reduction of aromatic rings in each repeat unit led to higher thermal decomposition rate. Furthermore, randomly distributed micro-defects in the SPEEK morphology and an increase in water sorption can reduce the fatigue strength of membranes in the wet-dry cycles. Eventually, hydrogen cross-over rate was gradually increased, and henceforth, accelerated destructive radical formation and degradation can be predicted.     

Deep-piRNA: Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features

Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development. Due to the vital roles of the piRNA in computational biology, the identification of piRNAs has become an important area of research in computational biology. This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods. The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process. The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method. The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59% and 2.81% at layer I and layer II respectively. It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine.     

Synthesis of Au/UiO-66-NH2/Graphene composites as efficient visible-light photocatalysts to convert CO2

The production of new solar fuel through CO₂ photocatalytic reduction has aroused tremendous attention in recent years because of the increased demand of global energy sources and global warming caused by the mass concentration of CO₂ in the earth's atmosphere. In this work, UiO-66-NH₂ was co-modified by the Au nanoparticles (Au-NPs) and Graphene (GR). The resultant nanocomposite exhibits a strong absorption edge in visible light owing to the surface plasmon resonance (SPR) of Au-NPs. More attractively, Au/UiO-66-NH₂/GR displays much higher photocatalytic activity (49.9 μmol) and selectivity (80.9%) than that of UiO-66-NH₂/GR (selectivity: 71.6%) and pure UiO-66-NH₂ (selectivity: 38.3%) for the CO₂ reduction under visible light. The enhanced photocatalytic performance is primarily dued to the surface plasmon resonance (SPR) of Au-NPs, which could enhance the visible light absorption. The GR sheets could play as an electron acceptor with superior conductivity and thus suppress the recombination of electrons and holes. Besides, the GR could also improve the dispersibility of UiO-66-NH₂ so as to expose more active sites and strengthen the capture of CO₂. The contact effect and synergy effect among different samples are strengthened in the ternary composites and the photocatalytic performance is therefore improved. This study demonstrates a MOF based hybrid composite for efficient photocatalytic CO₂ reduction, the findings not only prove great potential for the design and application of MOFs-based materials but also bring light to novel chances in the development of new high performance photocatalysts.