Formula Optimization of Emulsifiers for Preparation of Multiple Emulsions Based on Artificial Neural Networks

Formulation optimization of emulsifiers for preparing multiple emulsions was performed in respect of stability by using artificial neural network (ANN) technique. Stability of multiple emulsions was expressed by the percentage of reserved emulsion volume of freshly prepared sample after centrifugation. Individual properties of multiple emulsions such as droplet size, δ, viscosity of the primary and the multiple emulsions were also considered. A back‐propagation (BP) network was well trained with experimental data pairs and then used as an interpolating function to estimate the stability of emulsions of different formulations. It is found that using mixtures of Span 80 and Tween 80 with different mass ratio as both lipophilic and hydrophilic emulsifiers, multiple W/O/W emulsions can be prepared and the stability is sensitive to the mixed HLB numbers and concentration of the emulsifiers. By feeding ANN with 39 pairs of experimental data, the ANN is well trained and can predict the influences of several formulation variables to the immediate emulsions stability. The validation examination indicated that the immediate stability of the emulsions predicted by the ANN is in good agreement with measured values. ANN therefore could be a powerful tool for rapid screening emulsifier formulation. However, the long‐term stability of the emulsions is not good, possibly due to the variation of the HLB number of the mixed monolayers by diffusion of emulsifier molecules, but can be greatly improved by using a polymer surfactant Arlacel P135 to replace the lipophilic emulsifier.     

Copper-containing dendromesogens: the influence of the metal on the mesomorphism

The synthesis and liquid crystalline behaviour of the first and second generations of a dendrimeric structure based on poly(propyleneimine)(DAB-dendr(NH₂)_x) are reported. 4-(4-n-Alkoxybenzoyloxy)salicylaldehydes are used as mesogenic moieties attached at the peripheral amino groups of the dendrimers giving rise to dendromesogens with four and eight mesogenic branches. From these dendromesogens, considered as organic ligands, were prepared six metal-containing dendrimers which incorporate two or four copper atoms in their structures. All the dendrimeric ligands and three of the metal-containing dendrimers exhibit liquid crystalline properties which were studied by optical microscopy, DSC, X-ray diffraction and EPR spectroscopy.     

Assessment of mobility of heavy metals in two soil types by use of column leaching experiments and chemometric evaluation of elution curves

The objectives of this study were to evaluate the mobility of heavy metals (HMs) in two types of soils (acidic forest soil and neutral agricultural soil) by leaching with calcium chloride solution in column experiments. The screening properties of neutral agricultural soil towards pollution by heavy metals (Ni, Cu, Zn and Cd) are approximately 10 times higher than those of acid forest soil. The neutral agricultural soil, polluted artificially by one pore volume (PV) of an HMs solution of concentration 200 mg L^?1, can screen the leaching of these metals over several hundreds of years. The higher apparent desorption rate and per cent desorption of HMs (especially Cd) in acid forest soil indicated a higher potential of intensive migration of the metals across the profile and indicated potential risk of Cd pollution for this type of soil. The latest approach of artificial neural networks to describe transport of HMs in soil has been also evaluated. Using a simple three-layer perceptron topology with three hidden neurons, the experimental data could be simulated. The results suggested that the pH of soil is a major factor controlling the retention of the heavy metals in the soils.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

广东省能源需求预测模型构建及实证分析

为了对广东省的能源需求进行准确的预测,首先分析了影响广东省能源需求的各种因素,构建了预测指标体系.在此基础上,针对能源系统非线性等复杂系统特征,结合粒子群算法和BP神经网络的优点,构建了改进的PSO-BP神经网络的预测模型,并通过主成分分析法对指标体系进行数据降维,以降低神经网络的规模和复杂程度.以广东省1985-2013年的能源需求数据进行模拟与仿真,并对2014-2018年的能源需求量进行预测,理论分析和实证研究表明,该方法能够很好的反映广东省能源需求的特征,预测结果较为准确合理.     

Preparation and properties of ZnMoO4 anode materials with polymer network gel method

Polymer network gel method combines the advantages of solid-phase method and liquid phase method, triggering acrylamide (AM) radical polymerization in aqueous solution and N, N′- methylene bis acrylamide (MBAM) active double bond cross-linking reaction, forming polymer chains to form a three-dimensional network. The polymer network space formed by the gel is bound and evenly distributed to the ions in the solution, thereby reducing the contact and aggregation of molecules and achieving the purpose of uniform particle size and small particle size. The principle diagram of network gel is shown in Figure. Using cubic zinc acetate and ammonium molybdate tetrahydrate as raw materials, cubic ZnMoO₄ negative electrode materials were prepared with polymer network gel method. The polymer network gel method has various effects on the structure, morphology and electrochemical properties of materials. Besides, the calcination temperature and calcination time were also the key factors to the electrochemical properties of the materials. In this paper, the effects of the ratio of monomer and crosslinker, calcination temperature and calcination time on ZnMoO₄ materials were studied by single variable method, the preparation process was optimized, and its characterization and electrochemical tests were carried out. After 100 cycles, the optimized ZnMoO₄ electrode has a discharge capacity of 374.0 mAh· g^?1, 332.5, 263.5 and 177.1 mAh · g^?1 at current densities of 0.1, 0.5, 1.0 and 2.0 A g^?1, respectively. The electrochemical results show that the optimized ZnMoO₄ has high capacity, large rate capability and excellent cycle stability.     

Coating Cellulosic Material with Ag Nanowires to Fabricate Wearable IR-Reflective Device for Personal Thermal Management: The Role of Coating Method and Loading Level

Textiles coated with silver nanowires (AgNWs) are effective at suppressing radiative heat loss without sacrificing breathability. Many reports present the applicability of AgNWs as IR-reflective wearable textiles, where such studies partially evaluate the parameters for practical usage for large-scale production. In this study, the effect of the two industrial coating methods and the loading value of AgNWs on the performance of AgNWs-coated fabric (AgNWs-CF) is reported. The AgNWs were synthesized by the polyol process and applied onto the surface of cotton fabric using either dip- or spray-coating methods with variable loading levels of AgNWs. X-ray diffraction, scanning electron microscopy (SEM), infrared (IR) reflectance, water vapor permeability (WVP), and electrical resistance properties were characterized. The results report the successful synthesis of AgNWs with a 30 μm length. The results also show that the spray coating method has a better performance for reflecting the IR radiation to the body, which increases with a greater loading level of the AgNWs. The antibacterial results show a good inhibition zone for cotton fabric coated by both methods, where the spray-coated fabric has a better performance overall. The results also show the coated fabric with AgNWs maintains the level of fabric breathability similar to control samples. AgNWs-CFs have potential utility for cold weather protective clothing in which heat dissipation is attenuated, along with applications such as wound dressing materials that provide antibacterial protection.     

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer

Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example, the high distinction among people, size of bladder variation and noise interference. Based on the above issues, we propose an MD-Unet network structure, which uses multi-scale images as the input of the network, and combines max-pooling with dilated convolution to increase the receptive field of the convolutional network. The results show that the proposed network can obtain higher precision than the existing models for the bladder cancer dataset. The MD-Unet can achieve state-of-art performance compared with other methods.     

Exploring artificial neural networks to model interatomic and intermolecular potential energy surfaces

We demonstrate how a back-propagation artificial neural network can be trained to represent a potential energy surface (PES) in a formless manner with limited data points and exploited to predict interaction energies for configurations not included in the training set. A similar exercise is undertaken for predicting the eigenvalues and eigenvectors of a model Hamiltonian matrix that delicately depends on parameters and exhibits crossing of eigen values.