Evaluation method for assessing the auxiliary service transaction mechanism in regional power markets

Accurate and seamless auxiliary services in the power market can guarantee smooth and continuous power system operation. China’s new round of power system reform has entered a critical period, and reform implementation requires comprehensive improvements in the maturity of the supporting auxiliary service market. This study reviews the development status and evolution path of the European unified power market and the US regional power market, provides experience for the development of China’s regional power market, then identifies the key influencing factors of auxiliary service trading mechanism design in regional power markets. To analyze the rationality of the auxiliary service trading evaluation index, this paper established an evaluation model for assessing regional power markets. Using combined weight optimization, the gray correlation TOPSIS method was applied to comprehensively evaluate auxiliary service trading in the regional power market. Finally, the application of the proposed evaluation method was briefly analyzed to examine four regional power markets in China and evaluate the effectiveness of current market construction in different regions and provide suggestions for future market construction.

Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy

In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, many scholars have tried different techniques. Unfortunately, there is a lack of research on Redis’s transaction in the existing literatures. This paper proposes a transaction model for key-value NoSQL databases including Redis to make possible allowing users to access data in the ACID (Atomicity, Consistency, Isolation and Durability) way, and this model is vividly called the surfing concurrence transaction model. The architecture, important features and implementation principle are described in detail. The key algorithms also were given in the form of pseudo program code, and the performance also was evaluated. With the proposed model, the transactions of Key-Value NoSQL databases can be performed in a lock free and MVCC (Multi-Version Concurrency Control) free manner. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.

Parallel Binomial American Option Pricing under Proportional Transaction Costs

We present a parallel algorithm that computes the ask and bid prices of an American option when proportional transaction costs apply to trading in the underlying asset. The algorithm computes the prices on recombining binomial trees, and is designed for modern multi-core processors. Although parallel option pricing has been well studied, none of the existing approaches takes transaction costs into consideration. The algorithm that we propose partitions a binomial tree into blocks. In any round of computation a block is further partitioned into regions which are assigned to distinct processors. To minimise load imbalance the assignment of nodes to processors is dynamically adjusted before each new round starts. Synchronisation is required both within a round and between two successive rounds. The parallel speedup of the algorithm is proportional to the number of processors used. The parallel algorithm was implemented in C/C++ via POSIX Threads, and was tested on a machine with 8 processors. In the pricing of an American put option, the parallel speedup against an efficient sequential implementation was 5.26 using 8 processors and 1500 time steps, achieving a parallel efficiency of 65.75%.

An Object-Oriented Transaction Model

In object-oriented database systems(OODBSs),the traditional transaction models are no longer suit- able because of the difference between the object-oriented data model(OODM)and the conventional da- ta models(e.g.relational data model).In this paper,transaction models for advanced databuse applica- tions are reviewed and their shortcomings are analyzed.Exchangeability of operations is proposed in- stead of commuativity and recoverability for using more semantics in transaction management.As a result,an object-oriented transaction model(in short,OOTM)is presented.It is not modeled for some special application,but directly based on object-oriented paradigms.A transaction is regarded as an interpretation of a method.Each transaction(even subtransactions)keeps relative ACID(Atomicity, Consistency,Isolation,Durability )properties,therefore the special problems appearing in OODBSs such as“long transactions”,“visibility of inconsistent database state”can be solved.

Optimal Dividend-Equity Issuance Strategy in a Dual Model with Fixed and Proportional Transaction Costs

In this paper, we consider the problem of optimal dividend payout and equity issuance for a company whose liquid asset is modeled by the dual of classical risk model with diffusion. We assume that there exist both proportional and fixed transaction costs when issuing new equity. Our objective is to maximize the expected cumulative present value of the dividend payout minus the equity issuance until the time of bankruptcy,which is defined as the first time when the company＇s capital reserve falls below zero. The solution to the mixed impulse-singular control problem relies on two auxiliary subproblems： one is the classical dividend problem without equity issuance, and the other one assumes that the company never goes bankrupt by equity issuance.We first provide closed-form expressions of the value functions and the optimal strategies for both auxiliary subproblems. We then identify the solution to the original problem with either of the auxiliary problems. Our results show that the optimal strategy should either allow for bankruptcy or keep the company＇s reserve above zero by issuing new equity, depending on the model＇s parameters. We also present some economic interpretations and sensitivity analysis for our results by theoretical analysis and numerical examples.

Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA

The bank transactions are needed to be modeled to predict the future transactions of the banks based on the previous transactions.In order to achieve efficient modeling of bank data transactions,Deep Belief Network(DBN)and Neural network(NN)classifiers are used in this paper.Initially,the bank transaction data such as transaction count and amount are subjected to feature extraction to extract the statistical features.Now,the extracted data are modeled using the combination of DBN and NN models,where the average modeled output from both the network is considered as the final result.The above procedure is utilized for the two prediction models such as transaction count and transaction amount.Moreover,the transaction count from prediction model 1 is subjected to the Auto-Regressive Integrated Moving Average(ARIMA)model to compute the relationship between the transition count and transition amount.Here,as the main contribution,the number of hidden neurons in both DBN and NN are optimized or tuned accurately using the hybridized optimization models with Lion Algorithm(LA),and Artificial Bee Colony(ABC)named L-ABC model.The average of entire transactional amounts,i.e.the modeled outputs are matched with the actual data to validate the performance of the implemented model.

Portfolio Optimization Model with Transaction Costs

The purpose of the article is to formulate, under the ∞ risk measure, a model of portfolio selection with transaction costs and then investigate the optimal strategy within the proposed. The characterization of a optimal strategy and the efficient algorithm for finding the optimal strategy are given.

Analysis of Specialized Production of Transaction Services Based on Essential Services Quantity

The specialized production of e-commerce transaction systems is an important research topic, which is of great significance for evaluating the development level and forecasting the development direction of e-commerce. However, there is a certain disparity between the current mainstream research model and reality, which leads to a deviated result. This paper puts forward a definition of ＂transaction efficiency＂ based on essential transaction services and establishes a model of the middleman＇s specialized production decision of transaction services. The research result shows that （1） transaction efficiency plays an important role in improving the middleman＇s specialized production level, （2） only when the transaction efficiency is higher than a certain threshold will the distribution middleman appear, and （3） the degree of economic specialization, price of commodities and transaction services, and other associated factors also affect the evolution of e-commerce transaction systems.

Ensuring consistency of Web services transaction

Composite Web services provide promising prospects for conducting cross-organizational Web service transactions. Such transactions generally require longer processing time and manipulate financially critical data. To efficiently manage these Web services transactions, isolation is commonly relaxed, but inconsistency will be caused by concurrently executing isolation-relaxing transactions. This article proposes an extension to the WS-Business Activity Protocol, which ensures the consistent executions of isolation-relaxing Web service transactions, and which is based on transaction dependency graphs distributed over multiple nodes. Furthermore, this article presents several algorithms implementing the protocol, and introduces the implement of a prototype system.

CVaR-hedging and its applications to equity-linked life insurance contracts with transaction costs

This paper analyzes Conditional Value-at-Risk(CVaR)based partial hedging and its applications on equity-linked life insurance contracts in a Jump-Diffusion market model with transaction costs.A nonlinear partial differential equation(PDE)that an option value process inclusive of transaction costs should satisfy is provided.In particular,the closed-form expression of a European call option price is given.Meanwhile,the CVaR-based partial hedging strategy for a call option is derived explicitly.Both the CVaR hedging price and the weights of the hedging portfolio are based on an adjusted volatility.We obtain estimated values of expected total hedging errors and total transaction costs by a simulation method.Furthermore,our results are implemented to derive target clients’survival probabilities and age of equity-linked life insurance contracts.

基于Logit模型的技术供需交易影响因素分析

将技术供方和专利视为整体,将供需双方是否通过某专利交易视为被解释变量,系统研究技术特征、组织实力和供需方多维邻近关系等对技术交易的影响。基于Logit模型对燃料电池领域专利数据实证研究发现:(1)考虑三维特征建立Logit模型,对交易形成预测的精度高达83.3%;(2)技术特征、组织实力特征、多维邻近特征对技术交易均有显著正向影响;(3)多维邻近特征对技术交易的促进程度远高于其他维度特征,供需双方信任是交易形成的关键因素。最后,从建立高质量技术供需数据库、技术交易信任体系、技术交易网络及交易推荐机制等方面提出对策思考。

基于节点影响力的区块链匿名交易追踪方法

随着区块链技术的快速发展,借助虚拟货币进行非法交易的行为越来越普遍,且数量仍在快速增长。为打击该类犯罪行为,目前主要从网络分析技术和图数据挖掘等角度研究区块链交易数据,以进行区块链交易追踪。然而,现有的研究在有效性、普适性以及效率等方面存在不足,且无法对新注册地址进行有效追踪。针对上述问题,文中提出了一种基于节点影响力的账户余额模型区块链交易追踪方法NITT,旨在追踪特定目标账户模型地址的主要资金流向。相比传统方法,该方法引入时间策略,降低了图数据规模,同时采用多重权重分配策略,筛选出了更有影响力的重要账户地址。在真实数据集上进行实验,结果表明,所提方法在有效性、普适性和效率等方面具有较大的优势。

我国数据要素市场治理的模式、现状与对策

要推动数据要素市场高质量发展,首先需要构建科学高效的市场治理机制。本文对数据要素市场治理素现状进行了梳理,提出市场化治理、垂直型治理和关系型治理等三种治理模式,并比较其特点,分析其现状与瓶颈。在此基础上,本文指出我国需要同时采用多种治理模式,形成以市场化治理为主、垂直型治理为辅、关系型治理作为重要补充的数据要素市场治理格局,并就三种治理模式的进一步完善提出了对策建议。

可再生能源配额制下风光储联合参与现货市场的交易决策

可再生能源配额制与现货市场建设的并行推进引起风电发电商、光伏发电商及储能电站(风光储)联合参与现货市场的交易决策发生变化。通过构建双层交易决策模型,对可再生能源配额制下风光储联合参与日前电能量及调频辅助服务市场的交易决策进行求解分析。模型上层以风光储总利润最大化为目标,同时考虑到风光储的内部调控,求解风光储最优交易策略;下层实现日前电能量和调频辅助服务市场的联合出清及绿色证书市场的清算。通过库恩-塔克(Karush-Kuhn-Tucker,KKT)条件、强对偶定理等方法将双层模型转换为混合整数线性规划问题进行求解。最后,通过算例验证了所提模型的有效性。结果表明:所提框架与模型能够优化风光储报量报价决策;可再生能源配额制的引入丰富了风光储的套利手段,风光储内部调控更加活跃;风光渗透率及配额制考核权重的变化对风光储的交易决策有重要影响。

员工对企业数字化转型的反应研究:基于压力的理论视角

在数字经济蓬勃发展的时代背景下,数字化转型已成为推动企业高质量发展的关键环节。然而,很多企业在数字化转型过程中都遇到缺乏员工支持、消极合作等来自企业内部的挑战。目前,有关企业数字化转型的研究主要集中在宏观战略层面,探讨数字化转型对企业内部员工影响的研究尚不充足。因此,本文从企业内部微观层面着手,采用工作压力动态过程模型,将数字化转型视作一种压力源。通过聚焦数字化转型给员工带来的工作压力,试图揭示员工对数字化转型产生的差异化反应及其内在机理。第一,探究压力源,基于扎根理论归纳员工对数字化转型中压力源的感知和识别。第二,追踪压力结果,基于变革分析框架,探索数字化转型对员工工作绩效的影响及其随时间的动态变化。第三,分析压力体验,分别从认知和情绪视角,探讨员工支持或抗拒数字化转型的作用机制、边界条件及后续结果。本文不仅丰富了企业数字化转型在人力资源管理领域的研究范畴和内容,还为企业科学推动数字化转型、提升员工对数字化转型的支持水平提供了实践启示。

支持混合事务和分析处理的数据库管理系统综述

数据库管理系统根据应用场景分为事务型(OLTP)系统和分析型(OLAP)系统.随着实时数据分析需求增长,OLTP任务和OLAP任务混合的场景越来越普遍,业界开始重视支持混合事务和分析处理(HTAP)的数据库管理系统.这种HTAP数据库系统除了需要满足高性能的事务处理外,还需要满足实时分析对数据新鲜度的要求.因此,对数据库系统的设计与实现提出了新的挑战.近年来,在工业界和学术界涌现了一批架构多样、技术各异的原型和产品.综述HTAP数据库的背景和发展现状,并且从存储和计算的角度对现阶段的HTAP数据库进行分类.在此基础上,按照从下往上的顺序分别总结HTAP系统在存储和计算方面采用的关键技术.在此框架下介绍各类系统的设计思想、优劣势以及适用的场景.此外,结合HTAP数据库的评测基准和指标,分析各类HTAP数据库的设计与其呈现出的性能与数据新鲜度的关联.最后,结合云计算、人工智能和新硬件技术为HTAP数据库的未来研究和发展提供思路.

Optimal dividend and capital injection problem with a random time horizon and a ruin penalty in the dual model

In the dual risk model, we consider the optimal dividend and capital injection problem, which involves a random time horizon and a ruin penalty. Both fixed and proportional costs from the transactions of capital injection are considered. The objective is to maximize the total value of the expected discounted dividends, and the penalized discounted both capital injections and ruin penalty during the horizon, which is described by the minimum of the time of ruin and an exponential random variable. The explicit solutions for optimal strategy and value function are obtained, when the income jumps follow a hyper-exponential distribution.Besides, some numerical examples are presented to illustrate our results.