Estimates of daily oxygen consumption,carbon dioxide and methane emissions,and heat production for beef and dairy cattle using spot gas sampling

A simulation study was conducted to examine accuracy of estimating daily O₂ consumption, CO₂ and CH₄ emissions, and heat production (HP) using a spot sampling technique and to determine optimal spot sampling frequency (FQ). Data were obtained from 3 experiments where daily O₂ consumption, emissions of CO₂ and CH₄, and HP were measured using indirect calorimetry (respiration chamber or headbox system). Experiment 1 used 8 beef heifers (ad libitum feeding; gaseous exchanges measured every 30 min over 3 d in respiration chambers); Experiment 2 used 56 lactating Holstein-Friesian cows (restricted feeding; gaseous exchanges measured every 12 min over 3 d in respiration chambers); Experiment 3 used 12 lactating Jersey cows (ad libitum feeding; gaseous exchanges measured every hour for 1 d using headbox style chambers). Within experiment, averages of all measurements (FQALL) and averages of measurements selected at time points with 12, 8, 6, or 4 spot sampling FQ (i.e., sampling every 2, 3, 4, and 6 h in a 24-h cycle, respectively; FQ12, FQ8, FQ6, and FQ4, respectively) were compared. Within study a mixed model was used to compare gaseous exchanges and HP among FQALL, FQ12, FQ8, FQ6, and FQ4, and an interaction of dietary treatment by FQ was examined. A regression model was used to evaluate accuracy of spot sampling within study [i.e., FQALL (observed) vs. FQ12, FQ8, FQ6, or FQ4 (estimated)]. No interaction of diet by FQ was observed for any variables except for CH₄ production in experiment 1. No FQ effect was observed for gaseous exchanges and HP except in experiment 2 where CO₂ production was less (5,411 vs. 5,563 L/d) for FQ4 compared with FQALL, FQ12, and FQ8. A regression analysis between FQALL and each FQ within study showed that slopes and intercepts became farther from 1 and 0, respectively, for almost all variables as FQ decreased. Most variables for FQ12 and FQ8 had root mean square prediction error (RMSPE) less than 10% of the mean and concordance correlation coefficient (CCC) greater than 0.80, and RMSPE increased and CCC decreased as FQ decreased. When a regression analysis was conducted with combined data from the 3 experiments (mixed model with study as a random effect), results agreed with those from the analysis for the individual studies. Prediction errors increased and CCC decreased as FQ decreased. Generally, all the estimates from FQ12, FQ8, FQ6, and FQ4 had RMSPE less than 10% of the means and CCC greater than 0.90 except for FQ6 and FQ4 for O₂ consumption and CH₄ production. In conclusion, the spot sampling simulation with 3 indirect calorimetry experiments indicated that FQ of at least 8 samples (every 3 h in a 24-h cycle) was required to estimate daily O₂ consumption, CO₂ and CH₄ production, and HP and to detect changes in those in response to dietary treatments. This sampling FQ may be considered when using techniques that measure spot gas exchanges such as the GreenFeed and face mask systems.     

Microstructure and coloring mechanism of iron spots on bluish white porcelain from Jingdezhen of the Song Dynasty

Jingdezhen is famous for its bluish white (Qingbai) porcelains of the Song Dynasty, and those decorated with iron spots are distinctive among them. Herein, iron spots on a bluish white porcelain were investigated using a series of microscopic and spectroscopic characterizations. We found the decreasing iron content from more than 8 wt% to about 2 wt% during the glaze color transition from rusty to brown and finally into green, which built a connection on the coloring mechanism of iron-rich crystallized glaze and celadon glaze. We identified the rare ε-Fe₂O₃, a promising magnetic material, in both the dark brown crystals and the triangular crystals in the rusty area, which is its first discovery among bluish white porcelains. Based on these findings, we discussed the coloring mechanism of iron-spot decoration along with the physical form of the iron oxide crystals, indicating the partially reducing atmosphere during firing process.     

Effect of metallurgical evolution during post-weld aging treatment on localised corrosion of resistance spot welding joints of A286 superalloy

The combined effect of resistance spot welding and precipitation hardening on the localised corrosion of A286 superalloy is studied. The specimens tested by double loop electrochemical potentiokinetic reactivation were welded in the solution treated condition, and then subjected to different precipitation hardening treatments. For both base metal and weld nugget, the maximum localised corrosion is reached when η phase is clearly observable. The fact that the localised corrosion resistance of weld nugget is different from that shown by base metal may be explained by the segregation of Ni and Ti towards the interdendritic region of weld nugget (studied by using scanning electron microscope/energy dispersive X-ray analysis).     

中国天然气市场可持续发展分析

承接2016年回暖之势,2017年中国天然气市场迎来爆发式增长,全年天然气消费量达2 335×10~8 m~3、年消费增量达353×10~8 m~3。为此,有必要剖析推动中国天然气市场爆发式增长的主要因素并预测其未来发展的可持续性。研究结果表明:(1)2017年爆发式增长主要受益于宏观经济形势好转、大气污染防治措施深入实施、化工等大宗产品价格回升、华东地区天然气发电负荷增加、管道天然气供应价格相对较低、LNG汽车市场整体呈现回暖态势等因素;(2)经济发展形势、"煤改气"的推进、政策实施方向、油气价格趋势等外部因素将能继续支持天然气市场的快速增长;(3)但天然气产业的自身条件、资源和基础设施条件将对市场发展造成制约。结论认为:(1)2020年之前我国天然气市场需求量仍会保持快速增长的趋势,但难以维持每年300×10~8 m~3的增长规模;(2)工业燃料和发电是天然气市场发展的主要方向;(3)环渤海等地区是未来天然气需求量增长的主要区域。为了保持我国天然气市场的发展态势,提出如下建议:(1)千方百计扩大天然气供应规模,在认真做好国产非常规天然气资源开发的同时,积极落实进口天然气资源;(2)毫不松懈地建设基础设施,管道要超前建设,LNG接收站要加大力度建设,地下储气库等调峰设施要鼓励建设;(3)科学合理地理顺天然气价格,居民价格与非居民价格并轨首当其冲,确立价格是平衡供需关系的杠杆作用,减少行政干涉。     

天然橡胶期货市场回顾及后市展望

2019年天然橡胶期货市场起起落落,沪胶价格继续低位震荡。2020年天然橡胶供大于求的局面仍不会发生根本性改变,由极端天气引发的天然橡胶市场行情变动可能会减小,汽车行业可能会在政策支持下再次振兴,尤其重型卡车市场会成为亮点,轮胎企业继续寻求扩大出口。2020年上半年,天然橡胶期货市场可能还会受到2019年异常天气和病害等的余波作用,胶价会达到一个相对的高点,价格底部抬升,但很难出现持续性上涨的市场行情。2020年下半年,随着天然橡胶产量的季节性增长,胶价可能会再次回落。     

Machine learning methods for short‐term bid forecasting in the renewable energy market: A case study in Italy

In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play.     

Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

This article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimization problems of a price-taking generation company (GenCo) in the electricity market. To simulate the binary-nature PBUC problem, the continuous, real-value whale position/location is mapped into binary search space through various transfer functions. This article introduces three variants of BWOA using tangential hyperbolic, inverse tangent (arctan) and sigmoidal transfer functions. The effectiveness of the BWOA approaches is examined in test systems with different market mechanisms, i.e. an energy-only market, and energy and reserve market participation with different reserve payment methods. The simulation results are presented, discussed and compared with other existing approaches. The convergence characteristics, solution quality and consistency of the results across different BWOA variants are discussed. The superiority and statistical significance of the proposed approaches with respect to existing approaches is also presented.     

A general framework to study the price‐color relationship in paintings with an application to Mark Rothko rectangular series

We propose a general framework to study the relationship between the price of a painting and its color‐related attributes. To this end we focus on four key aspects: dominant colors, features of the color palette, color harmony, and color emotions. We demonstrate the usefulness of this approach with an example based on Mark Rothko's post‐1950 paintings (the “rectangular” series), and auction data from the 1994 to 2018 period. We identify two distinct price‐color regimes in Rothko's market: (a) [1994‐2005], a period in which prices are explained mainly by the growing popularity of the artist regardless of the color attributes of the paintings sold; and (b) [2006‐2018], a period in which color‐related attributes explain most of the prices. Furthermore, we find that in this second period, the dominant colors and the diversity of the color palette, are by far the most relevant attributes that influence the price; color harmony and color emotions hold almost no explanatory power during this period. Finally, we propose a new metric based on the Herfindahl Index to describe color diversity; this metric seems to be promising at characterizing the effect of the color palette on the price of a painting.     

Wholesale electricity spot market design: Find a way or make it

Designing wholesale electricity spot markets even after three decades of experience in dozens of countries continues to be a challenging task. The challenges arise from a myriad of options that exist without adequate level of unbiased guidance, and experiences in real-life markets where successes and failures are also open to the interpretation of experts. This is particularly a critical issue in the developing world where wholesale spot markets are yet to be established in 80 % of the countries and the experience in the remaining 20 % countries with an operational market, is mixed. There are certain trends in selection of market design that leave significant room for improvement including fallacies that should be avoided. Implementation of markets have also been problematic in most cases including unrealistic timetable and inadequate consideration of transition mechanism that have also hampered market development. In this article, we present our views on how policy makers, stakeholders and development institutions should think about the choices around market design and some of the common pitfalls around its implementation.