Particle size distribution and homogenisation efficiency in high-pressure homogenisation of wheat germ oil-water system

Wheat germ oil (WGO) is well-known as a good source of vegetable oil due to its nutrients and health benefits. Emulsification is a process that improves the incorporation of oil into food. High-pressure homogenisation (HPH) is a nonthermal and soft technique with enormous potential in oil-in-water emulsification. This paper focussed on the application of HPH for emulsification of WGO-in-water system. Influences of homogenisation pressure (100–300 bar), oil fraction (10–20% v/v) and lecithin adding (0–0.2% w/v of content) on the homogenisation were evaluated based on distribution of particles diameter and homogenisation efficiency. The increase in operating pressure and lecithin ratio decreased the particle size and increased the emulsion stability, and vice versa for oil fraction. The findings imply that the investigated factors significantly influenced particle size and emulsion system stability. The regression model between mean particle diameter and technical conditions of emulsion was established. With HPH treatment conditions of 300 bar operating pressure, 10% (v/v) oil fraction and 0.2% (w/v) lecithin created an emulsion system with a mean particle size of 3.32 µm, more than 50% of the volume of particles smaller than 1.5 µm of diameter and the homogenisation efficiency of 98.61%. HPH exhibits high efficiency and potential in WGO-in-water emulsification application.     

Two‐Terminal Multibit Optical Memory via van der Waals Heterostructure

2D van der Waals (vdWs) heterostructures exhibit intriguing optoelectronic properties in photodetectors, solar cells, and light‐emitting diodes. In addition, these materials have the potential to be further extended to optical memories with promising broadband applications for image sensing, logic gates, and synaptic devices for neuromorphic computing. In particular, high programming voltage, high off‐power consumption, and circuital complexity in integration are primary concerns in the development of three‐terminal optical memory devices. This study describes a multilevel nonvolatile optical memory device with a two‐terminal floating‐gate field‐effect transistor with a MoS₂/hexagonal boron nitride/graphene heterostructure. The device exhibits an extremely low off‐current of ≈10^?14 A and high optical switching on/off current ratio of over ≈10⁶, allowing 18 distinct current levels corresponding to more than four‐bit information storage. Furthermore, it demonstrates an extended endurance of over ≈10⁴ program–erase cycles and a long retention time exceeding 3.6 × 10⁴ s with a low programming voltage of ?10 V. This device paves the way for miniaturization and high‐density integration of future optical memories with vdWs heterostructures.     

A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam

Horizontal displacement of hydropower dams is a typical nonlinear time-varying behavior that is difficult to forecast with high accuracy. This paper proposes a novel hybrid artificial intelligent approach, namely swarm optimized neural fuzzy inference system (SONFIS), for modeling and forecasting of the horizontal displacement of hydropower dams. In the proposed model, neural fuzzy inference system is used to create a regression model whereas Particle swarm optimization is employed to search the best parameters for the model. In this work, time series monitoring data (horizontal displacement, air temperature, upstream reservoir water level, and dam aging) measured for 11 years (1999–2010) of the Hoa Binh hydropower dam were selected as a case study. The data were then split into a ratio of 70:30 for developing and validating the hybrid model. The performance of the resulting model was assessed using RMSE, MAE, and R ². Experimental results show that the proposed SONFIS model performed well on both the training and validation datasets. The results were then compared with those derived from current state-of-the-art benchmark methods using the same data, such as support vector regression, multilayer perceptron neural networks, Gaussian processes, and Random forests. In addition, results from a Different evolution-based neural fuzzy model are included. Since the performance of the SONFIS model outperforms these benchmark models with the monitoring data at hand, the proposed model, therefore, is a promising tool for modeling horizontal displacement of hydropower dams.

     

An efficient in‐network caching decision algorithm for Internet of things

Recently, content‐centric networking (CCN) has become one of the important technologies for enabling the future networks. Along with its recognized potentialities as a content retrieval and dissemination solution, CCN has been also recently considered as a promising architecture for the Internet of things (IoT), because of 2 main features such as named‐based routing and in‐network caching. However, IoT is characterized by challenging features: small storage capacity of resource‐constrained devices due to cost and limitation of energy and especially transient data that impose stringent requirements on the information freshness. As a consequence, the intrinsic caching mechanisms existing in CCN approach do not well suit IoT domains; hence, providing a specific caching policy at intermediate nodes is a very challenging task. This paper proposes an effective multiattribute in‐network caching decision algorithm that performs a caching strategy in CCN‐IoT network by considering a set of crucial attributes including the content store size, hop count, particularly key temporal properties like data freshness, and the node energy level. Simulation results proved that our proposed approach outperforms 2 cache management schemes (probabilistic least recently used and AlwaysCache–first in first out in terms of improving total hit rate, reducing data retrieval delay, and enhancing content reusability in IoT environment).     

Growth of Bacillus cereus on Oil-blanched Potato Strips for "Home-style" French Fries

Fresh cut, oil blanched strips from whole potatoes stored at 7 or 13 °C were inoculated with approximately 3 or 5 log CFU/g Bacillus cereus and incubated at 21 or 26.7 °C for up to 9 h to model handling of "home-style" French fries. Whole potato storage at 13 °C and incubation at 26.7 °C resulted in faster growth than 7 or 21 °C. Frying (2 to 3.5 min at 185 °C) inactivated up to 5.1 log B. cereus spores. Oil blanched potato strips for "home-style" French fries should be stored at £ 21 °C or finish fried or discarded within 3 to 4 h.     

Growth and Production of Enterotoxin A by Staphylococcus aureus on "Home-style" French Fries

Staphylococcus aureus strains were inoculated onto fresh-cut oil-blanched potato strips stored at 21 or 26.7 °C for up to 9 h to determine if the microorganism was capable of growth and staphylococcal enterotoxin (SE) production. Potato strips were assayed for SE using a commercial ELISA kit prior to and following finish frying. S. aureus increased by 2.5 to 2.8 log CFU/g over 9 h at 26.7 °C, and SE was detected after 5 h. SE remained serologically detectable following finish frying of the potato strips. It is recommended that oil-blanched potato strips stored at 26.7 °C be finish fried and served, or discarded, within 3 to 4 h to prevent possible production of SE.     

Assessment of SGS closure for isochoric combustion of hydrogen-air mixture

Direct numerical simulation (DNS) data of freely propagating turbulent premixed flame of stoichiometric hydrogen air mixture inside a closed vessel is analysed to study a sub-grid combustion closure based on unstrained flamelet approach. This modeling framework needs closures for the sub-grid scale (SGS) reaction rate and scalar dissipation rate. The results show that the closure models for these two SGS quantities work quite well. The dissipation rate closure involves a scale dependent parameter, ${\beta }_c$, which is related to the flame curvature induced effects. The reactivity of reactant mixture increases with time in isochoric combustion because the mixture temperature and pressure increase with time. This also influences the parameter ${\beta }_c$ and thus the dynamic evaluation of this parameter is investigated using the DNS data.     

Interphase transfer of tackifier between poly(butadiene) and poly(styrene-co-butadiene)

The interphase transfer behavior of poly(styrene-co-α-methyl styrene), which is known as a tackifier in rubber industry, is investigated using poly(butadiene) rubber (BR) and poly(styrene-co-butadiene) rubber (SBR). The films of pure rubbers and the blends with the tackifier were prepared by a solution-cast method using toluene as a solvent. Two sheets composed of different rubbers, in which the tackifier was mixed at least in one rubber sheet, were piled together and annealed beyond the glass transition temperature T _g of the tackifier. The transfer phenomenon of the tackifier between the sheets was evaluated by the peak shift in the tensile loss modulus E″ curve, ascribed to T _g of the rubbers, which was measured by the dynamic mechanical analysis. Moreover, crystallization temperature of BR was also employed as a measure of the transfer, because the tackifier retards the crystallization of BR. It is found that the tackifier moves from one rubber to another during annealing procedure to reduce severe localization. When the content is the same in each rubber, the tackifier immigrates from BR to SBR, suggesting a better miscibility with SBR. This behavior is attributed to the small difference in the solubility parameter between SBR and the tackifier as compared to that between BR and the tackifier.