Bovidae-based gelatin: Extractions method,physicochemical and functional properties,applications, and future trends

Gelatin is one of the most important multifunctional biopolymers and is widely used as an essential ingredient in food, pharmaceutical, and cosmetics. Porcine gelatin is regarded as the leading source of gelatin globally then followed by bovine gelatin. Porcine sources are favored over other sources since they are less expensive. However, porcine gelatin is religiously prohibited to be consumed by Muslims and the Jewish community. It is predicted that the global demand for gelatin will increase significantly in the future. Therefore, a sustainable source of gelatin with efficient production and free of disease transmission must be developed. The highest quality of Bovidae-based gelatin (BG) was acquired through alkaline pretreatment, which displayed excellent physicochemical and rheological properties. The utilization of mammalian- and plant-based enzyme significantly increased the gelatin yield. The emulsifying and foaming properties of BG also showed good stability when incorporated into food and pharmaceutical products. Manipulation of extraction conditions has enabled the development of custom-made gelatin with desired properties. This review highlighted the various modifications of extraction and processing methods to improve the physicochemical and functional properties of Bovidae-based gelatin. An in-depth analysis of the crucial stage of collagen breakdown is also discussed, which involved acid, alkaline, and enzyme pretreatment, respectively. In addition, the unique characteristics and primary qualities of BG including protein content, amphoteric property, gel strength, emulsifying and viscosity properties, and foaming ability were presented. Finally, the applications and prospects of BG as the preferred gelatin source globally were outlined.     

A Quest for (sila)0-4cyclopentasilylenes and their Arduengo Analogs by DFT

Silicon - Following our interest in exotic silylenes, here we compare and contrast 20 novel five-membered cyclic silylenes, including saturated (sila)0–4 cyclopentasilylenes (1–10) and...     

Effects of Prior Annealing on the Spark Plasma Sintering of Nanostructured Y2O3 Powders

The effects produced by annealing Y₂O₃ nanopowders on their spark plasma sintering (SPS) behavior are systematically investigated in this work. It is found that the annealed powders display higher sinterability with respect to the as‐received ones. Indeed, the maximum densification level reached from pristine powders is about 97.5%, whereas density decreases when further increasing either the sintering temperature or the dwell time. In contrast, the density of SPS products obtained from pretreated powder monotonically increases with temperature and processing time, thus leading to fully dense materials in 30 min at 1050°C and 60 MPa. Correspondingly, it is found that the annealing treatment markedly inhibits grain coarsening during SPS. Thus, dense translucent samples with grain size below 100 nm can be attained from annealed powders. On the other hand, white‐opaque specimens with significantly coarser microstructures (up to 1‐μm‐sized grains) are obtained when pristine powders are directly processed under the same sintering conditions. Furthermore, it is observed that the annealing treatment of SPS samples in air allows for graphite contamination removal, whereas no improvement in term of light transmittance is produced.     

Morphological evolution on the surface of hydrothermally synthesized hydroxyapatite microspheres in the presence of EDTMP

Well-ordered and surface engineered hierarchical hydroxyapatite microspheres (HAM) were prepared via a template free hydrothermal process. Ethylene diamine tetra (methylene phosphonic acid) (EDTMP) was used as chelating or regulating agent for the first time in this study. The results indicated the formation of sheet-like particles in the absence of EDTMP. On the other hand, microspheres with radially grown nanorods (HAMNR) or nanosheets (HAMNS) on the surface were obtained (with average diameter of 5?µm) in the presence of EDTMP. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy were used to characterize the crystalline phases in the synthesized samples. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that EDTMP concentration played an important part in regulating the morphology to form well organized microspheres with nanosheets or nanorods on the surface. Brunauer-Emmett-Teller (BET) revealed an increase in the specific surface area with the change in morphology from the HAMNS to HAMNR. Possible mechanisms are proposed to account for the formation of different morphologies based upon thermodynamic and kinetic theories.     

Trend Analysis and Spatial Prediction of Groundwater Levels Using Time Series Forecasting and a Novel Spatio-Temporal Method

Water Resources Management - Overexploitation of groundwater in the Malayer Plain has resulted in a continuous decline of groundwater levels over recent years with associated risks to water...     

Antimicrobial residues survey by LC-MS in food-producing animals in Lebanon

ABSTRACT

The treatment of animals with antimicrobial products may lead to the contamination of edible tissues by their residues, which may represent a risk to human health. Therefore, this study aimed to determine the level of antimicrobial residues in food-producing animals (chicken, beef, and milk) in Lebanon. A total of 310 samples were collected and analysed using an LC-MS/MS for the determination of 48 compounds belonging to different families in order to map their compliance according to the European Commission decision 2002/657/EC. Results show that 60% of the analysed samples were not contaminated by any residue, while 12% presented a concentration higher than the MRLs for tetracyclines, sulphonamides, quinolones, and macrolides. Results revealed that chicken were the most contaminated by antimicrobial residues, when compared to beef and milk. The obtained results demonstrate the uncontrolled use of antimicrobials in some Lebanese farms and claim for better management of livestock.     

A Simple Quasi-2D Numerical Model of a Thermogage Furnace

A simple quasi-2D model for the temperature distribution in a graphite tube furnace is presented. The model is used to estimate the temperature gradients in the furnace at temperatures above which contact sensors can be used, and to assist in the redesign of the furnace heater element to improve the temperature gradients. The Thermogage graphite tube furnace is commonly used in many NMIs as a blackbody source for radiation thermometer calibration and as a spectral irradiance standard. Although the design is robust, easy to operate and can change temperature rapidly, it is limited by its effective emissivity of typically 99.5–99.8%. At NMIA, the temperature gradient along the tube is assessed using thermocouples up to about 1,500°C, and the blackbody emissivity is calculated from this. However, at higher operating temperatures (up to 2,900°C), it is impractical to measure the gradient, and we propose to numerically model the temperature distributions used to calculate emissivity. In another paper at this conference, the model is used to design an optimized heater tube with improved temperature gradients. In the model presented here, the 2-D temperature distribution is simplified to separate the axial and radial temperature distributions within the heater tube and the surrounding insulation. Literature data for the temperature dependence of the electrical and thermal conductivities of the graphite tube were coupled to models for the thermal conductivity of the felt insulation, particularly including the effects of allowing for a gas mixture in the insulation. Experimental measurements of the temperature profile up to 1,500°C and radial heat fluxes up to 2,200°C were compared to the theoretical predictions of the model and good agreement was obtained.     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

A robust critical path in an environment with hybrid uncertainty

In this article, we consider the project critical path problem in an environment with hybrid uncertainty. In this environment, the duration of activities are considered as random fuzzy variables that have probability and fuzzy natures, simultaneously. To obtain a robust critical path with this kind of uncertainty a chance constraints programming model is used. This model is converted to a deterministic model in two stages. In the first stage, the uncertain model is converted to a model with interval parameters by alpha-cut method and distribution function concepts. In the second stage, the interval model is converted to a deterministic model by robust optimization and min-max regret criterion and ultimately a genetic algorithm with a proposed exact algorithm are applied to solve the final model. Finally, some numerical examples are given to show the efficiency of the solution procedure.