Study on the Price Design and Contract Stability of "Company + Farmer" Model with Time Preference under Double Moral Hazards

The double moral hazard of ＂company ＋ farmer＂ and the time preference cost of company and farmer was analyzed. According to static game model, it re-vealed that the reason for low compliance rate of ＂company ＋ farmer＂ model was the existence of market risk, namely, the fluctuation of market price, and the stable market price in contracts was actualy a kind of interval, instead of a specific value. Furthermore, the effect of default penalty, market transaction cost and time prefer-ence cost on the stability of contract was studied. The results showed that default penalty, market transaction cost and time preference cost had positive influence on the price interval range of a contract.

Effect of Reclamation Time and Land Use on Soil Properties in Changjiang River Estuary,China

The objective of this study is to analyze soil physical and chemical properties,soil comprehensive functions and impact factors after different years of reclamation.Based on the survey data taken from 216 soil sampling points in the Fengxian Reclamation Area of the Changjiang (Yangtze) River Estuary,China in April 2009 and remotely sensed TM data in 2006,while by virtue of multivariate analysis of variance (MANOVA),geo-statistical analysis (GA),prin-cipal component analysis (PCA) and canonical correspondence analysis (CCA),it was concluded that:1) With the in-crease in reclamation time,soil moisture,soil salinity,soil electric conductivity and soil particle size tended to decline,yet soil organic matter tended to increase.Soil available phosphorous tended to increase in the early reclamation period,yet it tended to decline after about 49 years of reclamation.Soil nitrate nitrogen,soil ammonia nitrogen and pH changed slightly in different reclamation years.Soil physical and chemical properties reached a steady state after about 30 years of reclamation.2) According to the results of PCA analysis,the weighted value (0.97 in total) that represents soil nutrient factors (soil nitrate nitrogen,soil organic matter,soil available phosphorous,soil ammonia nitrogen,pH and soil particle size) were higher than the weighted value (0.48 in total) of soil limiting factors (soil salinity,soil elec-tric conductivity and soil moisture).The higher the F value is,the better the soil quality is.3) Different land use types play different roles in the soil function maturity process,with farmlands providing the best contribution.4) Soil physi-cal and chemical properties in the reclamation area were mainly influenced by reclamation time,and then by land use types.The correlation (0.1905) of the composite index of soil function (F) with reclamation time was greater than that with land use types (-0.1161).

THE BENCHMARK LAND PRICE SYSTEM AND URBANLAND USE EFFICIENCY IN CHINA

ABSTRACT: China began to introduce market principles and establish price mechanism to better manage land and improve land use efficiency in the late 1980s. Since then, land markets begin to emerge. A benchmark land price system, providing guidelines for land use rights selling and transferring, was established in order to overcome lack of market data and experiences in land transaction. The benchmark prices of land use rights are determined by land use, land use density (floor-land ratio), land grades, land improvement, and tenant resettlement costs. This paper first conducts a formal analysis based on modern urban economic theory. The formal model provides a theoretical foundation in which the benchmark land price system is assessed and evaluated in terms of land use and urban development. The paper then concludes that the benchmark price system has two theoretical problems. One is associated with the fact that floor-land ratio plays an important role in land price determination whereas the theory suggests the other way around. That is, floor-land ratio depends on land prices. The other problem is that the benchmark land price system does not provide adequate room for the substitution between land and capital inputs. The substitution is a key in achieving land use efficiency in land markets and urban development process. It is concluded that the practice of the benchmark land price system is at odd with reforms that aim to introduce market principles and mechanism to guide resource uses. Therefore, it is recommended that further land policy reform should be taken.

Association between tear film break up time and blink interval in visual display terminal users

AIM：To investigate the association between tear film break up time（TBUT）and blinking interval in visual display terminal（VDT）users.METHODS：Nine hundred and thirty VDT users underwent dry eye testing,and functional visual acuity（FVA）test.The blinking interval during FVA was compared with TBUT.Subjects with longer blinking interval than TBUT were considered as unstable tear film.Logistic regression analysis revealed the risk factors for unstable tear group.RESULTS：Among 930 workers,858 subjects（92.3%）participated in this study.Almost 80% of the subjects were categorized into the unstable tear group.Unstable tear group has significantly lower Schirmer values and TBUT（17.5±11.6 vs 21.1±11.5 mm,3.7±2.6 vs 5.7±2.7s,both P〈0.001）.There were no significant differences in epithelial staining or severity of symptoms.Logistic regression showed that over 40y was a risk for being unstable tear group[odds ratio（OR）=1.53;95%confidence interval（CI）=1.06-2.20].Contact lens use was protective factor for being in the unstable tear group（OR=0.37;95%CI=0.26-0.53）.CONCLUSION：Subjects with shorter TBUT than blinking interval are prevalent among VDT users.Subjects over the age of 40 shows an increased risk for unstable tear film.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use(TOU)strategy can effectively improve the energy consumption mode of customers,reduce the peak-valley difference of load curve,and optimize the allocation of energy resources.This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use.First,this study proposes a period partitioning model,which is based on a moving boundary technique with constraint factors,and the Dunn Validity Index(DVI)is used as the objective to solve the period partitioning.Second,a control strategy for the curtailable flexible load is investigated,and a TOU strategy is utilized for further modifying load curve.Third,a price demand response strategy for adjusting transferable load is proposed in this paper.Finally,through the case study analysis of typical daily flexible load curve,the efficiency and correctness of the proposed method and model are validated and proved.

Prediction of metal futures price volatility and empirical analysis based on symbolic time series of high-frequency

The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018,and the sample was divided into 194 histogram time series employing symbolic time series.The next cycle was then predicted using the K-NN algorithm and exponential smoothing,respectively.The results show that the trend of the histogram of the copper futures earnings prediction is gentler than that of the actual histogram,the overall situation of the prediction results is better,and the overall fluctuation of the one-week earnings of the copper futures predicted and the actual volatility are largely the same.This shows that the results predicted by the K-NN algorithm are more accurate than those predicted by the exponential smoothing method.Based on the predicted one-week price fluctuations of copper futures,regulators and investors in China’s copper futures market can timely adjust their regulatory policies and investment strategies to control risks.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Oil-Price Forecasting Based on Various Univariate Time-Series Models

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

Price Prediction of Seasonal Items Using Time Series Analysis

The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

Examining Screen Time, Screen Use Experiences, and Well-Being in Adults

Screen technologies have been found to have adverse outcomes on people’s well-being and mental health if used excessively however findings have varied depending on the screen type being assessed. The impact of prolonged TV-watching on mental health has been well established, whereas the influence of computers, the internet, and mobile phones is still being debated. Research exploring total screen use in adults is surprisingly lacking. The current study examined the relationship between Screen Time and well-being in adults, including positive relationships, meaning, and loneliness. The study is possibly the first to investigate how much pleasure and meaning people feel during screen use and their mediating effects. Using a correlational study design, participants (N = 139) reported their hours spent on all screen devices per day, how much pleasure and meaning they experience during screen use on average, and their general well-being levels. Screen Time was not found to be significantly correlated with well-being;and screen use experiences did not mediate any of the screen time and well-being relationships. However, screen use meaning was positively associated with overall well-being and positive relationships. This finding prompts a review of the importance of screen time for well-being, suggesting that this may be a limited approach. Other factors related to screen quality may be equal if not more important for well-being. Limitations and implications for maintaining or enhancing well-being while using screen devices are discussed.

AR Model Based on Time Series Modeling for Predicting Egg Market Price in 2021

Eggs,as a meat consumer product in China,are closely related to the vegetable basket project.Exploring and predicting the future trend of egg market price is of great significance for stabilizing egg price and market supply.In this study,the time series AR model was used for fitting the egg market prices in the 66 d from January 1 to March 7,2021,and the delay operator nlag18 was used for white noise test,giving pr>probability of chisq<0.005.The time series was not a white noise series,and then the stationary series was used for modeling.The optimal model was selected as the AR series(BIC(3,0)),and finally,the egg market price model AM was obtained as X_(t)=9.0556+(1+0.8926)ε_(t),which was the optimal model.The model showed that the egg price fluctuations in 2021 will be clustered,and the later price will be significantly affected by external factors in the previous period.The dynamic prediction results of the model showed that the egg price would stop falling in March 2020,and the egg price would continue to slow down in March.

Real- Time Color Enhancement Method Used for Intelligent Mobile Terminals

In certain environments and under some conditions, the video images taken by the intelligent mobile video phones seem dark, and the colors are not bright or saturated enough.This paper presents an adaptive method to enhance the video image brightness visualization and the color performance depending on the certain hardware property and function parameters. The experimental results prove that this method can enhance the colors and the contrast of the video images, based on the estimated quality feature values of each frame, without using the extra Digital Signal Processor (DSP).

Lifestyle and Time Use: The Impact of Retirement on Health

Based on the data from the China Health and Retirement Longitudinal Study(CHARLS)in 2011 and 2013,this paper studied the impact of retirement on male and female workers’health and its impact mechanism under the mandatory retirement age system in China by using Fuzzy Regression Discontinuity Design(FRDD).The results indicated that retirement increased the probability of men assessing themselves as"healthy"by 25 percentage points and lowered the probability of suffering from chronic diseases for women by 26 percentage points.In terms of mechanism analysis,it was found that the remarkable increase in social interactions after retirement was the main reason for the improvement of health for male retirees,but not the reason for the lower probability of having chronic diseases for female retirees.The findings serve as important references for formulating policies regarding postponing retirement age and flexible retirement.

Design of Real-time Electricity Prices and Wireless Communication Smart Meter

Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Time varying congestion pricing for multi-class and multi-mode transportation system with asymmetric cost functions

This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.

基于电力线载波通信的智慧园区电力物联网精准时间同步方法

智慧园区各类新兴业务在电力物联网(power internet of things,PIo T)设备提供的数据支持下开展。这些业务具有严格的时间同步要求。如何在现有电力线载波通信(power line carrier,PLC)的基础上实现高精度、高可靠时间同步成为关键问题。针对上述问题,首先,该文建立基于PLC的智慧园区电力物联网精准时间同步网络模型,根据改进精准时间协议(precision time protocol,PTP)计算同步误差,在此基础上,建立基于数字锁相环的频率偏移补偿模型,降低累积误差;其次,提出站点(station,STA)时间同步误差最小化问题;最后,提出基于经验匹配的电力物联网精准时间同步算法,通过调整时间同步匹配成本,优化STA的时间同步路径选择策略。仿真结果表明,所提方法能有效提高时间同步精度。

基于深度强化学习的空气源热泵供热系统温度控制策略

空气源热泵(air source heat pump,ASHP)负荷具备良好的可调节特性,其建模的准确性和控制策略的设计是充分发挥其调节潜力的关键。文中考虑空气源热泵供热系统的储热特性,提出了基于深度强化学习(reinforcement learning,RL)的空气源热泵供热系统温度控制策略。首先建立了基于参数辨识的空气源热泵供热系统数学模型。其次建立了空气源热泵供热系统马尔可夫过程决策模型,并基于Q-Learning算法设计了供热系统深度强化学习控制策略。基于实际运行数据的仿真结果表明,本文提出的考虑供热延迟的供热系统数学模型能够准确预测供回水温度及室内温度变化情况,且所提出的基于深度强化学习的温度控制策略能够在维持用户室内温度在设定值的前提下,有效降低用电成本。

Geoacoustic Inversion Using Time Reversal of Ocean Noise

We present a passive geoacoustic inversion method using two hydrophones, which combines noise interferometry and time reversal mirror （TRM） techniques. Numerical simulations are firstly performed, in which strong fo- cusing occurs in the vicinity of one hydrophone when Green＇s function （GF） is back-propagated from the other hydrophone, with the position and strength of the focus being sensitive to sound speed and density in the bottom. We next extract the GF from the noise cross-correlation function measured by two hydrophones with 8025-m distance in the Shallow Water ＇06 experiment. After realizing the TRM process, sound speed and density in the bottom are inverted by optimizing focusing of the back-propagated GF. The passive inversion method is inherently environmentally friendly and low-cost.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.