关于创新基层党建工作的探索

我国社会不断发展,电力行业也处于飞速的发展壮大当中。电力企业如何做好自身管理工作,保持正确的发展方向,是当前电力企业党建工作的开展重点。面临电力企业改革与市场竞争环境的变化,电力企业的党建工作开展也需要与时俱进地进行创新。     

Forecasting Bordeaux wine prices using state-space methods

Generic Bordeaux red wine (basic claret) can be regarded as being similar to an agricultural commodity. Production volumes are substantial, they are traded at high frequency and the quality of the product is relatively homogeneous. Unlike other commodities and the top-end wines (which represent only 3% of the traded volume), there is no futures market for generic Bordeaux wine. Reliable forecasts of prices can to large extent replace this information deficiency and improve the functioning of the market. We use state-space methods with monthly data to obtain a univariate forecasting model for the average price. The estimates highlight the stochastic trend and the seasonality present in the evolution of the price over the period 1999 to 2016. The model predicts the path of wine prices out of sample reasonably well, suggesting that this approach is useful for making reasonably accurate forecasts of future price movements.     

A320系列货舱门常见故障分析与排除

本文依据A320货舱门工作原理,结合航线的实际工作经验,总结分析了货舱门无法正常开关的故障原因。从液压油路,电路图两个方面分析了货舱门的操作原理,目的是快速找出最可能故障件,减少航班延误,保障航班安全。     

一种多功能液压试验台控制系统设计

本文介绍了一种多功能液压试验台控制系统设计,液压控制模块的设计采用了模块化设计方式,可以缩短产品设计开发周期减少开发成本。液压系统采用LUDV负载反馈控制技术能实现对不同负载压力的多个执行元件同时系统流量自动按比例分配。     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.     

Forecasting and forecast narratives: The Bank of England Inflation Reports

We analyze the narratives that accompany the numerical forecasts in the Bank of England’s Quarterly Inflation Reports, 1997–2018. We focus on whether the narratives contain useful information about the future course of key macro variables over and above the point predictions, in terms of whether the narratives can be used to enhance the accuracy of the numerical forecasts. We also consider whether the narratives are able to predict future changes in the numerical forecasts. We find that a measure of sentiment derived from the narratives can predict the errors in the numerical forecasts of output growth, but not of inflation. We find no evidence that past changes in sentiment predict subsequent changes in the point forecasts of output growth or of inflation, but do find that the adjustments to the numerical output growth forecasts have a systematic element.     

Forecasting the urban skyline with extreme value theory

The world’s urban population is expected to grow fifty percent by the year 2050 and exceed six billion. The major challenges confronting cities, such as sustainability, safety, and equality, will depend on the infrastructure developed to accommodate the increase. Urban planners have long debated the consequences of vertical expansion—the concentration of residents by constructing tall buildings—over horizontal expansion—the dispersal of residents by extending urban boundaries. Yet relatively little work has predicted the vertical expansion of cities and quantified the likelihood and therefore urgency of these consequences.We regard tall buildings as random exceedances over a threshold and use extreme value theory to forecast the skyscrapers that will dominate the urban skyline in 2050 if present trends continue. We predict forty-one thousand skyscrapers will surpass 150 meters and 40 floors, an increase of eight percent a year, far outpacing the expected urban population growth of two percent a year. The typical tall skyscraper will not be noticeably taller, and the tallest will likely exceed one thousand meters but not one mile. If a mile-high skyscraper is constructed, it will hold fewer occupants than many of the mile-highs currently designed. We predict roughly three-quarters the number of floors of the Mile-High Tower, two-thirds of Next Tokyo’s Sky Mile Tower, and half the floors of Frank Lloyd Wright’s The Illinois—three prominent plans for a mile-high skyscraper. However, the relationship between floor and height will vary considerably across cities.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

Is the environment‐friendly factor attractive to customers when purchasing electric vehicles? Evidence from South Korea

In a bid to reduce greenhouse gas emissions, several countries worldwide are implementing policies to promote electric vehicles (EVs). However, contrary to expectations, the diffusion speed of EVs has been rather slow in South Korea. This study analyzes consumer preferences for the technological and environmental attributes of EVs and derives policy and environmental implications to promote market diffusion of EVs in South Korea. We conduct a choice‐based conjoint survey of 1,008 consumers in South Korea and estimate the consumer utility function using a mixed logit model considering consumer heterogeneity. Based on the consumer utility function, we analyze consumers' willingness‐to‐pay (WTP) for EV attributes such as driving range, charging method, charging time, autonomous driving function, carbon dioxide (CO₂) reduction rate, and purchase price. The results indicate that the current low acceptance of EVs is due to their relatively high price and lack of a battery charging technology that satisfies consumers' expectations of the charging method and time. One interesting finding is that Korean consumers have a relatively higher WTP for the CO₂ reduction rate of EVs than consumers in other countries; however, they do not consider CO₂ reduction over other technological attributes when choosing EVs. This implies that the rate of CO₂ reduction of EVs is not an important factor for South Korean consumers when buying EVs. We also calculate the effect of CO₂ reduction with the market penetration of EVs and find that CO₂ reduction through the diffusion of EVs depends on the country's electricity generation mix.     

Are betting returns a useful measure of accuracy in (sports) forecasting?

In an economic context, forecasting models are judged in terms not only of accuracy, but also of profitability. The present paper analyses the counterintuitive relationship between accuracy and profitability in probabilistic (sports) forecasts in relation to betting markets. By making use of theoretical considerations, a simulation model, and real-world datasets from three different sports, we demonstrate the possibility of systematically or randomly generating positive betting returns in the absence of a superior model accuracy. The results have methodological implications for sports forecasting and other domains related to betting markets. Betting returns should not be treated as a valid measure of model accuracy, even though they can be regarded as an adequate measure of profitability. Hence, an improved predictive performance might be achieved by carefully considering the roles of both accuracy and profitability when designing models, or, more specifically, when assessing the in-sample fit of data and evaluating out-of-sample forecasting performances.