Attitude-behaviour dissonance regarding the importance of food preservation for customers

The aim of the study was to identify the importance of individual food product attributes (the use of preservatives, processing method, shelf-life period and nutritional value) as well as their relation to the purchase behaviour. To achieve this, consumer preferences were decomposed in conditions of full access to information, and data was compared with actual consumer behaviour related to making purchasing decisions in front of the store shelf. Based on data from 338 respondents, conjoint analysis and repeated ANOVA measurements were carried out, allowing to eliminate individual behavioural patterns. The results showed a dissonance between the consumers attitudes towards the attributes and their shopping behaviour. The processing method was the most important declared attribute for consumers, meanwhile this information was the least searched for during purchase. At the same time, shelf-life period marked as the least important was the main information searched for by consumers when shopping. The results also show a clear dislike towards the use of microwaves as a preservation method, while traditional thermal preservation was marked as the most positive for consumers. In addition, consumers were classified into 3 heterogeneous groups, identifying differences in the preferences of food attributes. The obtained results have practical value relating not only to the preferences of individual attributes, but also to their various variants. The results can be a guide for companies to properly label food products. The indicated dissonance illustrates the educational gap and related insufficient motivation to get to know the product before buying it. This is extremely important in connection with technological development and new food processing methods.     

Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

齐大山铁矿南帮含水构造带探测研究

为解决采空区突发涌水给矿山安全生产带来的安全威胁,以齐大山铁矿为研究对象,以高密度电阻率法含水构造带理论模型的正演模拟研究为基础,运用高密度电阻率法对采场南帮含水构造带的异常范围进行了圈定;运用瞬变电磁法进行了二次探测,并对高密度电阻率法所圈定的异常范围进行了验证。研究表明:含水构造带显示为低阻异常特征,在高密度电阻率法探测成果图中显示的两处低阻异常与含水构造带理论模型的正演模拟结果一致;同时,在瞬变电磁法探测成果图中显示的低阻区域均在高密度电阻率法所圈定的低阻异常范围内,二者相互验证,这两处低阻异常均为含水构造带的地球物理异常特征的显示,表明采用高密度电阻率法和瞬变电磁法组合识别技术可实现对隐伏含水构造带的先导性探测。     

不同制样方法对直流辉光放电质谱法测定氮化硼中27种杂质元素的影响

讨论了3种制样方法对直流辉光放电质谱法(dc-GDMS)检测氮化硼中的Na、Mg、Al、Si等27种杂质元素的影响。3种制样方法分别如下所示:方法1,直接把氮化硼压在铟薄片上;方法2,把氮化硼压在铟薄片上后,再盖上一层铟罩;方法3,把压碎后的氮化硼放在针状钽勺上。在优化的辉光放电参数下对比了3种不同制样方法对基体信号强度的影响。试验表明:在方法1中,当氮化硼尺寸约为3mm×3mm,厚度小于1mm时,基体¹¹B的信号可达1.8×10⁷ cps;在方法2中,选择铟孔大小合适的铟罩,基体¹¹B的信号可达1.0×10⁷ cps;方法3获得基体信号强度比方法1、方法2高一个数量级。大部分元素在中分辨率下可获得较好的结果,而对于在高分辨率下也较难分离的元素,可选择丰度较低的同位素在中分辨率下进行测定,如Ge选择⁷⁰Ge⁺,Se选择⁸²Se⁺,Cd选择¹¹¹Cd⁺,Sn选择¹¹⁹Sn⁺,Ag选择¹⁰⁹Ag⁺,Pt选择¹⁹⁴Pt⁺。氮化硼中的杂质元素含量可通过样品片中待测元素含量减去来自于铟薄片或钽勺中该元素贡献的含量来计算获得。将样品平行测定5次,相对标准偏差均在20%以内。对于Al、Si、Ti等元素的测定,3种制样方法的测定结果基本一致;方法1、方法2中检测到的In含量较大,使得铟中的Ni、Cu对氮化硼的测定值影响较大;方法3由于钽中Fe、Cu的贡献导致氮化硼中Fe、Cu的检测值较大,但方法3获得的基体信号强度大,可降低部分元素的检出限,如Cr、Mn、Ga、Ge等。综上所述,方法3为优选方法。     

不同煤阶煤孔隙特征及其吸附能力响应

我国是煤炭资源大国，煤层气储量规模相当可观，但煤储层又具有低孔、低渗的不足，照搬国外或常规石油天然气的开采程序和方法已被证实是走不通的。应结合实际，根据不同地质条件、不同煤阶的开采程序，进行孔隙特征研究。煤孔隙特征、连通性和吸附能力对煤层气开采影响尤为重要，为研究煤孔隙结构特征随煤变质程度的变化关系及其吸附能力的响应特点，采取不同地区不同变质程度煤样，进行压汞测试和等温吸附实验。实验结果表明，煤孔隙度和平均孔径均随变质程度增加呈现降低—升高—降低的趋势；煤中孔隙连通性随煤变质程度增加逐渐变差；随煤变质程度增加，其最大吸附能力也呈现降低—升高—降低的总体趋势。     

马脊梁煤矿井下巷道贯通测量方法分析

在煤矿测量工作中,贯通测量是其中重要工作之一,确保贯通点的误差在设定的范围内,决定了贯通测量的成败。本文将马脊梁煤矿副立井与王村回风立井的贯通作为研究对象,选择使用井下控制测量、竖井联系测量及陀螺定向技术等,设定了贯通测量的方案,开展了误差预计,通过对比预计结果和要求,保证了贯通测量设计的可行性。     

不同有机物对硝酸盐氮测试的影响

研究了红糖、葡萄糖、乙酸钠、甲醇、乙醇、乙酸、丙酸、丁酸、正戊酸、混合有机溶剂等10种不同有机物对麝香草酚光谱法和离子色谱法测试硝酸盐氮的影响。结果表明,溶液中含有红糖或葡萄糖时,均会对麝香草酚法测试硝酸盐氮造成影响,使得测试结果偏大;在无硝酸根离子存在时,红糖和葡萄糖的质量浓度与硝酸盐氮的测试浓度非线性相关;而乙酸钠、甲醇、乙醇、乙酸、丙酸、丁酸、正戊酸和混合有机溶剂则对麝香草酚法测试硝酸盐氮没有影响。实验发现,红糖和葡萄糖在浓硫酸的作用下与麝香草酚发生Molisch反应,形成有色复合物和红棕色不溶性沉淀。实验采用离子色谱法测试硝酸盐氮时,红糖和葡萄糖等糖类有机物对测试结果均无影响。     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.