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.     

Mobolic: An automated approach to exercising mobile application GUIs using symbiosis of online testing technique and customated input generation

The increasingly prevalent use of mobile devices has raised the popularity of mobile applications. Therefore, automated testing of mobile applications has become an extremely important task. However, it is still a challenge to automatically generate tests with high coverage for mobile applications due to their specific nontrivial structure and the highly interactive nature of graphical user interfaces (GUIs). In this paper, we propose a novel automated GUI testing technique for mobile applications, namely, Mobolic. In this approach, tests with high coverage are automatically generated and executed by combining the online testing technique and customated input generation. Employing the online testing technique, Mobolic systematically explores the app GUI without falling in a loop. It generates relevant events “on the fly” that are followed by an immediate execution. In addition, involving the customated input generation, Mobolic automatically generates relevant user inputs such as user‐predefined, concrete, or random ones. We implemented Mobolic and evaluated its performance on 10 real‐world open‐source Android applications. Our experimental results show the effectiveness and efficiency of Mobolic in terms of achieved code coverage and overall exercising time.     

Layered Oxide Cathode for Potassium‐Ion Battery: Recent Progress and Prospective

Rechargeable potassium‐ion batteries (KIBs) have demonstrated great potential as alternative technologies to the currently used lithium‐ion batteries on account of the competitive price and low redox potential of potassium which is advantageous to applications in the smart grid. As the critical component determining the energy density, appropriate cathode materials are of vital need for the realization of KIBs. Layered oxide cathodes are promising candidates for KIBs due to high reversible capacity, appropriate operating potential, and most importantly, facile and easily scalable synthesis. In light of this trend, the recent advancements and progress in layered oxides research for KIBs cathodes, covering material design, structural evolution, and electrochemical performance are comprehensively reviewed. The structure–performance correlation and some effective optimization strategies are also discussed. Furthermore, challenges and prospects of these layered cathodes are included, with the purpose of providing fresh impetus for future development of these materials for advanced energy storage systems.     

Ultrathin Porous NiFeV Ternary Layer Hydroxide Nanosheets as a Highly Efficient Bifunctional Electrocatalyst for Overall Water Splitting

Herein, the hydrothermal synthesis of porous ultrathin ternary NiFeV layer double hydroxides (LDHs) nanosheets grown on Nickel foam (NF) substrate as a highly efficient electrode toward overall water splitting in alkaline media is reported. The lateral size of the nanosheets is about a few hundreds of nanometers with the thickness of ≈10 nm. Among all molar ratios investigated, the Ni_0.75Fe_0.125V_0.125‐LDHs/NF electrode depicts the optimized performance. It displays an excellent catalytic activity with a modest overpotential of 231 mV for the oxygen evolution reaction (OER) and 125 mV for the hydrogen evolution reaction (HER) in 1.0 m KOH electrolyte. Its exceptional activity is further shown in its small Tafel slope of 39.4 and 62.0 mV dec^?1 for OER and HER, respectively. More importantly, remarkable durability and stability are also observed. When used for overall water splitting, the Ni_0.75Fe_0.125V_0.125‐LDHs/NF electrodes require a voltage of only 1.591 V to reach 10 mA cm^?2 in alkaline solution. These outstanding performances are mainly attributed to the synergistic effect of the ternary metal system that boosts the intrinsic catalytic activity and active surface area. This work explores a promising way to achieve the optimal inexpensive Ni‐based hydroxide electrocatalyst for overall water splitting.     

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.     

Visualizing music similarity: clustering and mapping 500 classical music composers

Scientometrics - This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500?×?500 composers’ similarity/distance matrix. The objective is...     

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.

     

A valences-totaling model for English sentiment classification

Sentiment classification plays an important role in everyday life, in political activities, activities of commodity production and commercial activities. Finding a time-effective and highly accurate solution to the classification of emotions is challenging. Today, there are many models (or methods) to classify the sentiment of documents. Sentiment classification has been studied for many years and is used widely in many different fields. We propose a new model, which is called the valences-totaling model (VTM), by using cosine measure (CM) to classify the sentiment of English documents. VTM is a new model for English sentiment classification. In this study, CM is a measure of similarity between two words and is used to calculate the valence (and polarity) of English semantic lexicons. We prove that CM is able to identify the sentiment valence and the sentiment polarity of the English sentiment lexicons online in combination with the Google search engine with AND operator and OR operator. VTM uses many English semantic lexicons. These English sentiment lexicons are calculated online and are based on the Internet. We present a full range of English sentences; thus, the emotion expressed in the English text is classified with more precision. Our new model is not dependent on a special domain and training data set—it is a domain-independent classifier. We test our new model on the Internet data in English. The calculated valence (and polarity) of English semantic words in this model is based on many documents on millions of English Web sites and English social networks.     

Dietary patterns during pregnancy and risk of gestational diabetes: a prospective cohort study in Western China

Background

Previous research has demonstrated the association between maternal dietary patterns and gestational diabetes (GDM), but evidence in Asian populations remains limited and inconsistent. This study investigated the association between dietary patterns during early pregnancy and the risk of GDM among pregnant women in Western China.

Methods

A prospective cohort study was conducted among 1337 pregnant women in Western China. Dietary intakes were assessed at 15–20 weeks of gestation using a validated food frequency questionnaire. GDM was diagnosed by oral glucose tolerance tests at 24–28 weeks of gestation. Exploratory factor analysis was performed to derive dietary patterns, and logistic regression models were used to examine the association between dietary patterns and GDM.

Results

A total of 199 women (14.9%) developed GDM. Three dietary patterns were identified, namely, a plant-based pattern, a meat-based pattern and a high protein-low starch pattern. Notwithstanding a lack of association between dietary patterns and GDM risk in the whole cohort, there was a significant reduction in GDM risk among overweight women (BMI ≥24 kg/m²); the odds ratio being 0.29 (95% confidence interval 0.09 to 0.94) when comparing the highest versus the lowest score of the high protein-low starch pattern.

Conclusions

There was no significant association between early pregnancy dietary patterns and GDM risk later in pregnancy for women in Western China, but high protein-low starch diet was associated with lower risk for GDM among women who were overweight at pre-pregnancy.