Mileage-based pricing insurance and its impact on China

Mileage-based pricing insurance,such as PAYD,is known as the"green insurance"because it is low-carbon and environmentally effective,which is the biggest innovation in the global auto insurance industry seen in the past decade.Starting from the perspective of economic externalities,vehicle negative externalities in China are described.In order to introduce mileage-based pricing insurance to the Chinese insurance industry,this paper reviews the current practice of PAYD insurance and its impacts on transport externalities,including air pollution,climate change,energy dependency,congestion,accidents,and others.Finally,enlightenment and policy suggestions are proposed,in the hope of better promoting the low-carbon economy development over the whole of China.

A Research of Real-Time Pricing Mechanism and Its Characteristics

Real-Time Pricing (RTP) is proposed as an effective Demand-Side Management (DSM) to adjust the load curve in order to achieve the peak load shifting. At the same time, the RTP mechanism can also raise the revenue of the supply-side and reduce the electricity expenses of consumers to achieve a win-win situation. In this paper, a real-time pricing algorithm based on price elasticity theory is proposed to analyze the energy consumption and the response of the consumers in smart grid structure. We consider a smart grid equipped with smart meters and two-way communication system. By using real data to simulate the proposed model, some characteristics of RTP are summarized as follows: 1) Under the condition of the real data, the adjustment of load curve and reducing the expenses of consumers is obviously. But the profit of power supplier is difficult to ensure. If we balance the profits of both sides, the supplier and consumers, the profits of both sides and the adjustment of load curve will be relatively limited. 2) If assuming the response degree of consumers to real-time prices is high enough, the RTP mechanism can achieve the expected effect. 3, If the cost of supply-side (day-ahead price) fluctuates dramatically, the profits of both sides can be ensured to achieve the expected effect.

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.

Decomposition laws of tungsten prices fluctuation since 1900 and its applications

Tungsten current price was transformed yearly to its constant price since 1900, which is roughly decomposed into four components as trend, cycle, impact and random. The core prices, consisting of the trend and the cycle, present regularities that a long-run cycle is embedded within two major cycles, and major cycle is composed of low-price period and high-price period, along with the rapid rise into a tower, and along with deep down into next trough; three sharply upward shocks occur by the events in a tower. Fluctuations in prices trend to slow cycles and expand the bands. It can be expected that tungsten price will highly stand over 17 a, and is is a advice that reducing production and restricting export maybe maintain a high price level.

Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application

A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.

DIMENSIONALITY REDUCTION BASED ON SVM AND LDA,AND ITS APPLICATION TO CLASSIFICATION TECHNIQUE

Some dimensionality reduction （DR） approaches based on support vector machine （SVM） are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on SVM while ignoring the within-class information in data. This paper presents a new DR approach, call- ed the dimensionality reduction based on SVM and LDA （DRSL）. DRSL considers the between-class margins from SVM and LDA, and the within-class compactness from LDA to obtain the projection matrix. As a result, DRSL can realize the combination of the between-class and within-class information and fit the between-class and within-class structures in data. Hence, the obtained projection matrix increases the generalization ability of subsequent classification techniques. Experiments applied to classification techniques show the effectiveness of the proposed method.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

Artificial Intelligence-Based Automated Actuarial Pricing and Underwriting Model for the General Insurance Sector

The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.

Dynamic Pricing Strategies Using Artificial Intelligence Algorithm

Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the possible benefits and challenges will help companies to understand the impact of their chosen pricing strategies. AI-driven Dynamic pricing has great opportunities to increase a firm’s profits. Firms can benefit from personalized pricing based on personal behavior and characteristics, as well as cost reduction by increasing efficiency and reducing the need to use manual work and automation. However, AI-driven dynamic rewarding can have a negative impact on customers’ perception of trust, fairness and transparency. Since price discrimination is used, ethical issues such as privacy and equity may arise. Understanding the businesses and customers that determine pricing strategy is so important that one cannot exist without the other. It will provide a comprehensive overview of the main advantages and disadvantages of AI-assisted dynamic pricing strategy. The main objective of this research is to uncover the most notable advantages and disadvantages of implementing AI-enabled dynamic pricing strategies. Future research can extend the understanding of algorithmic pricing through case studies. In this way, new, practical implications can be developed in the future. It is important to investigate how issues related to customers’ trust and feelings of unfairness can be mitigated, for example by price framing.

Analyses of Current Electricity Price and Its Changing Trend Forecast in the Coming Five Years

This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period should satisfy the requirements of power industry restructuring.Therefore, it is necessary to set up an appropriate pricing mechanism and system including thelinks of sales price to network, transmission and distribution price (T&D price) and sales price.In the light of various factors influencing increase and decrease in price, a forecast of electricitytariff is given in the five years to come.[

Maximizing Supermarket Profits:Data-Driven Strategies for Pricing,Sales,and Forecasting

The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation.This paper explores methods to maximize profit through purchasing and sales strategies.Initially,the relevant data for various categories of vegetables is integrated.Through histograms,their sales patterns are directly understood,highlighting the most popular vegetables.Upon analyzing each vegetable category,it becomes evident that their sales data do not conform to normal distributions.Therefore,Spearman correlation coefficients are calculated,revealing strong correlations between certain categories,such as aquatic roots and edible fungi.A line chart depicting the top ten selling vegetables indicates a noticeable periodicity.Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing.To address this,additional factors such as holidays,weeks,and months are incorporated using techniques like random forest regression.This approach yields cost-plus pricing dependence curves that better capture the relationship,while effectively managing noise.Regarding sales volume prediction,the original data displays significant volatility,necessitating the handling of outliers using the threshold method.For missing data,linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy.Subsequently,Adam-optimized long short-term memory(LSTM)networks are utilized to forecast incoming quantities for the next seven days.By extrapolating from normal sales volume,market capacity is estimated,allowing for additional sales through discount strategies.This framework has the potential to increase original income by 1.1 times.

China’s Price System Jeopardizes its Grain Reserves

2008 is a year of bumper harvest in summer grain across China. The failure of numerous state-owned grain depots to purchase grain in times of bumper harvest, however, directly threatens grain reserve security and state control over grain prices in the upcoming year. An important factor underpinning the difficulty of state grain depots to purchase grain is the unwillingness of farmers to sell grain due to the excess of the current market price over the government "protected price" aimed at preventing cheap grain from harming farmers. When grassroots grain depots find themselves in trouble, foreign capital stealthily moves in by taking advantage of this situation. To fulfill grain storage tasks and receive various state subsidies, some state-owned grain depots have no alternative but to surreptitiously raise the purchase price. By contrast, some not so courageous state-owned grain depots can only borrow money to finance the purchase of commodity grain at market prices and subsequently figure out a way to pay back such loans. Behind such distorted grain purchase behavior lies a rough and rugged history of grain price reform in China.

Extended Fan's Algebraic Method and Its Application to KdV and Variant Boussinesq Equations

An extended Fan's algebraic method is used for constructing exact traveling wave solution of nonlinearpartial differential equations.The key idea of this method is to introduce an auxiliary ordinary differential equationwhich is regarded as an extended elliptic equation and whose degree Υ is expanded to the case of r>4.The efficiency ofthe method is demonstrated by the KdV equation and the variant Boussinesq equations.The results indicate that themethod not only offers all solutions obtained by using Fu's and Fan's methods,but also some new solutions.

Effects of Rice Straw and Its Biochar Addition on Soil Labile Carbon and Soil Organic Carbon

Whether the biochar amendment could affect soil organic matter （SOM） turnover and hence soil carbon （C） stock remains poorly understood. Effects of the addition of ^13C-labelled rice straw or its pyrolysed biochar at 250 or 350℃ to a sugarcane soil （Ferrosol） on soil labile C （dissolved organic C, DOC; microbial biomass C, MBC; and mineralizable C, MC） and soil organic C （SOC） were investigated after 112 d of laboratory incubation at 25℃. Four treatments were examined as （1） the control soil without amendment （Soil）; （2） soil plus ^13C-labelled rice straw （Soil＋Straw）; （3） soil plus 250℃ biochar （Soil＋B250） and （4） soil plus 350℃biochar （Soil＋B350）. Compared to un-pyrolysed straw, biochars generally had an increased aryl C, carboxyl C, C and nitrogen concentrations, a decreased O-alkyl C and C：N ratio, but similar alkyl C and δ^13C （1 742- 1 877 %）. Among treatments, significant higher DOC, MBC and MC derived from the new C （straw or biochar） ranked as Soil＋Straw〉Soil＋B250〉Soil＋B350, whilst significant higher SOC from the new C as Soil＋B250〉Soil＋Straw≈Soil＋B350. Compared to Soil, DOC and MBC derived from the native soil were decreased under straw or biochar addition, whilst MC from the native soil was increased under straw addition but decreased under biochar addition. Meanwhile, native SOC was similar among the treatments, irrespective of the straw or biochar addition. Compared to Soil, significant higher total DOC and total MBC were under Soil＋Straw, but not under Soil＋B250 and Soil＋B350, whilst significant higher total MC and total SOC were under straw or biochar addition, except for MC under Soil＋B350. Our results demonstrated that the application of biochar to soil may be an appropriate management practice for increasing soil C storage.

Program evaluation and its application to equipment based on super-efficiency DEA and gray relation projection method

For the gray attributes of the equipment program and its difficulty to carry out the quantitative assessment of the equipment program information, the gray relation projection method is simply reviewed. Combining the super-data envelopment analysis（DEA） model and the gray system theory, a new super-DEA for measuring the weight is proposed, and a gray relation projection model is established to rank the equipment programs. Finally, this approach is used to evaluate the equipment program. The results are verified valid and can provide a new way for evaluating the equipment program.

Meat consumption in China and its impact on international food security: Status quo, trends, and policies

1.Background China has seen drastic nutrition transition and food structure change with rapid economic growth in the past three decades.Specifically,the traditional fibre-dominated food system is being replaced by a western-style meat-dominated diet（Yu and Abler 2009;Tian and Yu 2013）.In traditional China meat was regarded as a rarity and normally consumed during festivals,now it has become daily food for most Chinese consumers.

Genetic Modeling of GIS-Based Cell Clusters and Its Application in Mineral Resources Prediction

This paper presents a synthetic analysis method for multi sourced g eo logical data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been sta tistical analysis of cells delimitated based on thoughts of random sampling. Tha t might lead to insufficient utilization of local spatial information, for a cel l is treated as a point without internal structure. We now take “cell clusters ”, i. e. , spatial associations of cells, as basic units of statistics, thus th e spatial configuration information of geological variables is easier to be dete cted and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi discriminating model for the clusters via genetic algor ithm. Both the right judgment rates and the in class vs. between class distan ce ratios are considered to form the evolutional adaptive values of the populati on. An application of the method in gold mineral resources prediction in east Xi njiang, China is presented.