The Localization of “Imported” Urban Form in Northeast China: The Case Study on Circular–Radial Space in Dalian City

After the “imported” urban forms, which originated from foreign cultures, were transplanted into Chinese cities, they generate brand-new urbanscape but suffer from a lack of cultural roots and a disconnection with the mainstream of contemporary urban planning. Thus, their current value and potential in urban renewal are questioned. The study takes the circular–radial space from the Baroque cities as an example to clarify the motivation of its import from the west to northeast China. It further clarifies their adaptive changes in form and function in the local urban context, through a case study on Dalian City. The study finds that different geometric patterns of existing circular–radial space were influenced by European, American, and Japanese urban planning theories to varying degrees, but with equal emphasis on symbolism and functionality. Their implementation in Dalian has a continuity in time and space. But due to the changes in topography, traffic, and planning concepts, their forms and functions tend to be independent, their connection weakens, and their importance recedes after the street network. The circular–radial space in Dalian led to distinctive urbanscape. But during their inheritance and transformation, the rationality of new forms and functions, as well as the necessity of continuing the initial ones need to be dialectically considered, so as to avoid dogmatic revival and antique reproduction. Finally, the study reconsiders the concept of “localization” of “imported” urban form, and constructs a general research pattern to provide a new perspective for understanding the transformation of similar types of urban forms.     

旅游景区三维立体网络覆盖方案的研究及应用

随着旅游业的发展,节假日各景区游客日益增多甚至爆满,同时游客对景区的网络质量要求也越来越高。在目前降本增效的大趋势下,如何利用有限的资源实现最好的覆盖显得尤为重要。本文根据张家界景区的实际地形、游客分布及建筑情况等提出了“点、线、面”的三维立体覆盖方案,全面提升景区无线网络覆盖能力,成功打造精品网络。     

Comparison between discrete and continuous analysis of facial expressions,elicited by bitter-tasting beverages in overweight and healthy-weight individuals

The aim of the present study was to compare the information provided by a discrete and continuous data analysis of two emotions (disgust and joy), elicited by bitter-tasting beverages (coffee, yerba mate infusion and grapefruit juice) in two groups with different body mass index (BMI): overweight group (25 < BMI < 30), and healthy-weight control group (18.5 < BMI ≤ 25). Participants (n = 66; 34 females, 32 males) evaluated a total of three consecutive sips of the same beverage (taking one sip every 20 s and registering a continuous video for 60 s). The Wilcoxon test (continuous analysis) showed some changes generated as the drinks were being consumed. The biggest difference was the expression of disgust for coffee in the high BMI group, at the first sip (0–10 s). It represented 27% compared to 2% in normal BMI. The continuous analysis allowed to observe the periods where the differences were greater (0–10 and 40–50 s).     

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.     

Process design and techno‐economic analysis of ethyl levulinate production from carbon dioxide and 1,4‐butanediol as an alternative biofuel and fuel additive

Carbon dioxide capture, utilization, and storage (CCUS) is one of the promising negative emission technologies (NET). Within various CCUS routes available, CO₂ conversion into fuels is one of the attractive options. Currently, most of CO₂ conversion into fuels requires hydrogen, which is expensive and consume large energy to produce. Hence, a different route of producing fuel from CO₂ by utilizing 1,4‐butanediol as the raw material is proposed and evaluated in this study. This alternative route comprises production of levulinic acid from the reaction between CO₂ and 1,4‐butanediol and production of ethyl levulinate, an alternative biofuel and biofuel additive, via an esterification reaction of levulinic acid with ethanol. The process is designed and simulated according to the available data and evaluated in terms of its technical features. Because of the unavailability of reaction data for synthesis of levulinic acid from 1,4‐butanediol and CO₂, several assumptions were taken, which may implicate the accuracy of the studied design. This technical evaluation is followed by cost estimations and sensitivity analysis. Because of the free CO₂, the profitability of the plant depends strongly on the prices of the other chemicals and the price difference between 1,4‐butanediol (raw material) and ethyl levulinate (product). Monte Carlo simulation indicates that the proposed plant will always be profitable if the ethyl levulinate is slightly more expensive than the 1,4‐butanediol, highlighting that the process of producing ethyl levulinate from CO₂ is economically profitable. Future research should be directed towards a catalytic system that can effectively convert CO₂ into levulinic acid, by‐products produced from the two reaction steps, and reduce the excess ethanol used in the second reaction.