Farmers' preferences for adopting on-farm concentration of raw milk: Results from a discrete choice experiment in Germany

Milk concentrates are used in the manufacturing of dairy products such as yogurt and cheese or are processed into milk powder. Processes for the nonthermal separation of water and valuable milk ingredients are becoming increasingly widespread at farm level. The technical barriers to using farm-manufactured milk concentrate in dairies are minimal, hence the suspicion that the practice of on-farm raw milk concentration is still fairly uncommon for economic reasons. This study, therefore, set out to investigate farmers' potential willingness to adopt a raw milk concentration plant. The empirical analysis was based on discrete choice experiments with 75 German dairy farmers to identify preferences and the possible adoption of on-farm raw milk concentration. The results showed that, in particular, farmers who deemed the current milk price to be insufficient viewed on-farm concentration using membrane technology as an option for diversifying their milk sales. We found no indication that adoption would be impeded by a lack of trustworthy information on milk processing technologies or capital.     

以新发展理念引领高校专业基础课教学改革

以“创新、协调、绿色、开放、共享”为内核的新发展理念，是对马克思主义发展理念的继承和发扬，极具时代精神，富含问题意识，为高校思想政治教育发展、教育教学改革实践提供了强大的理论支撑。本文以“通信原理”为例，阐述了新发展理念在课程改革中的思路和方法，实现了思想政治教育与专业基础课程有机融合，为深化高校教学改革、创新人才培养模式提供了思路。     

变分自编码器模型综述

变分自编码器（VAE）作为深度隐空间生成模型的一种，近年来其表现性能取得了极大的成功，尤其是在图像生成方面。变分自编码器模型作为无监督式特征学习的重要工具之一，可以通过学习隐编码空间与数据生成空间的特征映射，进而在输出端重构生成输入数据。梳理了传统变分自编码器模型及其衍生变体模型的发展与研究现状，并就此做了总结和对比，最后分析了变分自编码器模型存在的问题与挑战，并就可能的发展趋势做了展望。     

瞬态闭锁试验在0.13 μm大规模集成电路中引起的潜在损伤

瞬态剂量率辐射试验会引起集成电路发生损伤或失效，其原因至少有两种：闭锁大电流引起的电路内部金属互连熔融；累积电离总剂量引起的氧化层电荷造成阈值电压偏移。本文以一种0.13 μm体硅CMOS处理器为对象，研究了瞬态剂量率和稳态电离总剂量辐射效应规律。结果表明：瞬态剂量率闭锁效应对处理器造成了显著的潜在损伤，导致其总剂量失效阈值从1 030 Gy(Si)降低至600 Gy(Si)。研究结论对于大规模集成电路的可靠性评估和指导辐射加固设计有重要参考意义。     

高性能三水醋酸钠-尿素-膨胀石墨混合相变材料的制备及其在电地暖中的应用性能

采用尿素调节三水醋酸钠的相变温度到合适范围再添加膨胀石墨来降低过冷度，研制了高性能的三水醋酸钠-尿素-膨胀石墨混合相变材料，并对其在电地暖中的应用性能进行了研究。结果表明，当尿素质量分数为36.5%、膨胀石墨添加量为4%(质量)时，所得混合相变材料的熔化焓高达209.1 J/g，熔点在31.98℃，过冷度仅为2.04℃，热导率为2.349 W/(m·K)，热可靠性良好；将用该混合相变材料制成的相变板安装在实验房的电地暖中时，实验房的热舒适度随着相变材料层厚度的增加而增加，但也带来加热时间和用电量的增加；当相变材料层厚度为10 mm时，电加热温度适宜设置在45℃；在热舒适度相当的条件下，有相变板的实验房与无相变板的参比房相比具有用电量小及电费低的优势。     

基于Flowmaster的相变空冷器元件开发及应用

基于湿空气热力学理论,以Flowmaster换热元件为基础,通过引入相变计算开发了考虑相变潜热的空冷器元件。经计算验证,所开发的元件计算结果与理论计算值吻合性较好,相同边界条件下,总换热量偏差和含湿量偏差均小于1%。分析表明:在高温、高湿环境条件下,湿空气会在空冷器中形成冷凝水,产生大量相变潜热,而Flowmaster换热元件模型由于未考虑相变因素,气侧出口温度计算结果偏低。采用所开发的相变空冷器元件模型可有效提高气侧出口温度计算的准确性,减小因相变带来的仿真误差。     

Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.     

Similarities and differences between male and female novice designers on color‐concept associations for warnings,action required,and signs and equipment status messages

This research examined the male and female novice designers toward color associations for the concepts used for ‘warnings’, ‘action required’, and ‘signs and equipment status’ through a questionnaire‐based study. A total of 178 Hong Kong Chinese final year undergraduate design students (89 males and 89 females) participated in the study. The test used required the participants to indicate their choice of one of nine colors to associations with each of 38 concepts in a color‐concept table, so that any one color could be associated with any one of the concepts. For both male and female groups of novice designers, chi‐square tests revealed a strong color association for each concept tested in this study (P < .05). The results showed males and females agreed on some color‐concept association stereotypes which were therefore gender neutral. The male and female novice designers had the same color associations and similar levels of stereotype strengths for 21 concepts. The nine strongest and therefore most useful color‐concept association stereotypes for both male and female novice designers were: red‐danger, red‐fire, red‐hot, red‐stop, red‐emergency, red‐error, blue‐cold, blue‐male, and green‐exit. However, the male and female novice designers had different color association stereotypes for the standby (green vs. yellow), emergency exit (green vs. red), and toxic (purple vs. black) concepts, and the strengths of the 14 remaining associations for both groups were not at equivalent levels. Overall, it is anticipated that the findings of this study will act as a useful reference for novice designers and other design practitioners to optimize color coding in the design of ‘warnings’, ‘action required’, and ‘signs and equipment status’ messages.     

融合时间隐语义填充和子群划分的推荐算法

随着个性化推荐技术的发展，推荐系统面临着越来越多的挑战。传统的推荐算法通常存在数据稀疏性和推荐精度低等问题。针对以上问题，提出了一种融合时间隐语义填充和子群划分的推荐算法[K]-TLFM（Time Based Latent Factor Model Integrated with [k]-means）。该算法利用融合时间因素的隐语义模型对原始用户物品评分矩阵缺失项进行填充，避免了用全局平均值或者用户/物品平均值补全矩阵带来的误差，有效缓解了数据稀疏性问题，同时融合时间因素有效地刻画了用户偏好随时间的变化；完成评分矩阵缺失项填充后，基于二分[k]-means聚类算法将偏好、兴趣特征相似的对象划分到同一个子群中，在目标用户所属的子群中基于选定的协同过滤算法为用户产生推荐列表，提高了推荐效率和准确性。在MovieLens和Netflix数据集上对该算法的推荐性能进行了对比实验，结果表明该算法具有更高的推荐精度。