Big data-driven discriminatory pricing not only creates opportunities to boost hotel profits but also amplifies consumers’ negative perceptions of price fairness. Developing a dynamic discriminatory pricing model with fairness constraints helps hotel room managers formulate optimal pricing strategies. This paper proposes a dynamic discriminatory pricing model with fairness constraints that unifies four pricing models: fixed pricing, dynamic pricing, discriminatory pricing, and dynamic discriminatory pricing. It further proposes a two-stage deep reinforcement learning algorithm to efficiently solve the model and generate optimal pricing strategies. Finally, a case study is conducted to validate the proposed model and algorithm. The results show that the two-stage deep reinforcement learning algorithm can instantaneously derive optimal pricing schemes that satisfy both group and temporal fairness constraints, following a reasonably time-efficient training process. By adjusting the fairness parameters, our model can be transformed into the four types of pricing models, and the performance of the algorithm is validated for the commonly used dynamic pricing and dynamic discriminatory pricing models. Compared to traditional nonlinear programming solution algorithms, this algorithm generates optimal daily prices based on real-time market changes, making it more practically applicable.

We present the first differentially private framework for stochastic frontier analysis (SFA), addressing the challenge of non-convex objectives in privacy-preserving efficiency estimation. We construct a bounded parameter space to control gradient sensitivity and adapt the Frank-Wolfe algorithm with calibrated linear oracle noise to mitigate cumulative perturbation. Incorporating l1-regularization facilitates sparse and interpretable variable selection under strict (& varepsilon;,delta)-differential privacy. Experiments demonstrate 15-35% MAE reduction under & varepsilon;=0.1, along with strong scalability and estimation accuracy compared to prior DP methods for non-convex models.

Optimizing pump scheduling in multiproduct pipelines can significantly reduce energy consumption and carbon emissions. For pump scheduling in multiproduct pipelines, to describe hydraulic losses more accurately, the model needs to adopt shorter discrete time intervals, which will lead to longer decision-making time. It combined with the large solution space of large-scale pipelines, will lead to low solution efficiency with dynamic programming methods and poor solution quality with heuristic optimization algorithms. Given that, this article develops a multiproduct refined oil transmission simulation system and employs the enhanced Proximal Policy Optimization (PPO) algorithm with action space shaping trick to optimize pump scheduling for large-scale multiproduct pipelines. The method of converting discrete action space to multi-discrete action space through action shaping can address PPO's low convergence efficiency issue resulting from the large discrete action space challenge common in large-scale multiproduct pipelines. The experimental results indicate that the proposed method, that is, PPO algorithm with multidiscrete action space exhibits significant advantages in terms of efficiency and robustness in large-scale pipelines compared to mainstream methods for pump scheduling such as dynamic programming (DP), genetic algorithms (GA), and ant colony optimization (ACO). Furthermore, we demonstrate the effectiveness of action space shaping in large-scale pipelines from the perspectives of exploration and exploitation. PPO algorithm with action space shaping interacts with the refined oil transmission pipeline system.image

"四新"背景下教学改革逐渐深入高校课堂,高效学习是改革的目标,其核心理念是师生在教学中的主体性回归.本文引入"学习同盟"概念,并提出了构建方法,以某高校"创业基础"教学课堂为应用对象,通过建立师生帮助同盟(HAQ)和学生协作同盟(CAQ)量表对学习同盟进行测量,基于结构方程模型研究了学习同盟对于学习效果的影响.学习同盟有利于实现主体角色的重塑,提升课堂教学成效,该模式可以为高校课堂教学改革提供借鉴.

智能工业转型和能源转型是我国重要的发展战略,二者互相促进有利于实现协同转型.利用2007-2020 年我国30 个省份的面板数据,构建智能工业转型与区域能源转型互动效应模型,实证检验互动效应的存在性并研究互动关系的形成机理,分析我国智能工业转型和区域能源转型的演化特征及互动效应的区域差异.研究发现:2007-2020 年间,智能工业转型与区域能源转型水平均呈现持续增长趋势,二者之间存在相互促进的互动效应;智能工业转型对区域能源转型的促进作用主要源于企业效益,而区域能源转型对智能工业转型的影响则源于政策驱动;融资环境、企业运营和环境政策等变量对这种互动关系产生了显著的正向调节作用,并存在明显的空间异化特征.

The carbon emissions issues majorly caused by energy consumption have become a public concern around the globe. As a major energy consumer and carbon emitter in the world, it is imperative for China to improve energy efficiency. Moreover, energy efficiency evaluation can provide evidence for policy formulating. This study measures and analyzes the total-factor energy efficiencies of China's provincial and comprehensive economic zones during 2008–2017 based on the novel three-stage Data Exchange Agreement (DEA)model, which is upgraded by introducing the super-efficiency Slacks-Based Measurement (SBM) model with the undesirable output into the traditional three-stage DEA model. The empirical results illustrate that environmental regulation, economic development, and technical innovation are significantly affect total-factor energy efficiencies. The total-factor energy efficiencies and their rankings of different provinces and eight comprehensive economic zones in China change significantly after removing the environmental variables and statistical noise. The energy efficiencies of eight comprehensive economic zones show a decreasing trend from “the east, the central to the west”, and the gap between them are also widening. This study provides policy implications and empirical evidence for China to improve energy efficiency, reduce carbon emissions and consequently, achieve the green sustainable development goals.

智能工业与能源可持续转型存在互动效应,二者协同转型更有利于加快传统工业转型升级.在构建智能工业转型指数( IITI)和区域能源可持续转型指数( RETI)的基础上,基于因子分析和耦合协调方法对智能工业与区域能源可持续协同转型水平进行测度,研究2007—2018年我国30个省份协同转型的时空分异特征,并基于探索性空间数据分析方法分析了协同转型的空间关联格局演化特征.结果表明:2007—2018年我国智能工业与区域能源可持续协同转型水平虽呈现出不断提升的趋势,但整体水平不高,2018年初级协调及以上水平的省份仅占20%,处于失调状态省份高达40%;协同转型呈现了"东部沿海+长江经济带"的" T型"格局,"虹吸效应"使得欠发达地区形成同步滞后现象;"南强北弱"特征较为明显,北方省份普遍低低聚集,协同转型形势严峻;未来应充分重视智能工业和区域能源可持续转型的高耦合关系,发挥发达地区的"辐射作用",引导资本、人才、技术向转型滞后地区流动,避免出现"锈带化"危机.

文章基于面板数据回归方法,使用青岛制造业15个行业2005-2018年的数据,在固定资本投入、R&D投入、环境规制等传统影响因素的基础上引入智能工业发展水平和行业间技术溢出效应,研究了各因素对生态效率的影响.结果显示,青岛制造业生态效率经过2005-2013年的持续增长后开始呈现衰退趋势,15个行业中有12个属于高消耗高排放和低消耗高排放类型,生态形势严峻;智能工业发展对于生态效率产生了正向促进作用,在各因素中占据主导地位,但其效果在2013年后出现了明显下滑;行业间技术溢出的作用并未得到体现,环境规制和R&D投入的影响效果也不突出,中低端产能的扩张对生态效率产生了严重的抑制作用.未来发展中应充分利用新技术带来的智能工业发展契机,严格限制盲目的产能扩张,提高面向生态环境的R&D投入和行业间技术溢出的作用效果.

Eco-efficiency is of great significance in the attainment of a sustainable society, and is the most severe issue facing industrial sectors in China. In this work, a hybrid super-efficiency data envelopment analysis (DEA) was conducted to measure eco-efficiency and analyze the problem of inefficiency facing industrial sectors in China. DEA has been widely used in eco-efficiency analysis; however, existing DEA models focus on the evolution and comparison of eco-efficiency values, and do not consider the evolution characteristics of the input nor undesirable output inefficiency and neglect the radial and non-radial classifications of input and output indices simultaneously. Thus, this work proposes a hybrid super-efficiency DEA model which combines hybrid DEA with super-efficiency DEA, separating the input and undesirable output variables into radial and non-radial parts using variable correlations. For the panel data of the industrial sectors, the Malmquist index is introduced in this approach. Subsequently, this approach is illustrated using a real data set from 22 industrial sectors in China corresponding to the period of 2006–2015. Results show that eco-efficiencies of the 22 industrial sectors increased consistently from 2006 to 2015. Due to a lack of momentum in sustained growth, technological progress regarding the improvement of eco-efficiency requires acceleration. More and more sectors have achieved rapid enhancements in input and undesirable output inefficiencies, but 10 traditional sectors with high resource consumption and heavy emissions of pollutants remain inefficient.

OBE模式与翻转课堂的结合使得教学方式更有利于培养目标的达成,文章针对信息管理与信息系统专业信息系统安全课程,使用布鲁姆目标分类理论确定课程的培养目标,选取SET协议模拟实验系统设计项目,使用理查德课程设计模型构建翻转实验课堂的组织步骤及考核体系.结果 表明,OBE理念下以学生为主体的翻转实验课堂教学模式使学生学习目标更为明确,团队合作意识更强,同时知识的综合运用能力和创新能力均能得到有效提升,相比于传统实验课堂,翻转实验课堂对于培养目标的达成更为有效.

能耗控制是油气产业节能减排和产业升级的重要内容,驱动因素与产业能耗指标演化是制定产业发展政策的依据.本文基于系统动力学方法对油气产业能耗驱动指标的因果关系进行分析,构建系统动力学(SD)模型,使用2007-2016年数据对山东省油气产业进行模拟仿真.结果 显示:经济环境因素中GDP增长对上游产业能源消耗量的影响最大,上游产业投资也是影响能耗增长的重要因素;整体科技水平的提高是最有效提高上游能源效率的因素.GDP增长率和投资也是影响下游产业能源消耗量的关键因素,第一产业GDP的占比、整体科技水平以及下游产业科技水平的提升可能会对下游产业能源消耗产生一定的降低作用.下游能源效率相对于上游能源效率明显较低,第三产业GDP占比的提升有助于下游产业能源效率的大幅度提升.经济规模、产业结构、产业投资规模是山东省油气产业能耗的最重要驱动因素,而产业结构调整对于油气产业能源消耗的影响并不明显.科技水平提升是油气产业能耗的重要控制因素,也是能源效率提升的决定因素.山东省整体科技水平提升对于油气产业能耗影响大于产业内部科技水平提升.国际油价上涨会推动上游产业能耗增加,而下游产业能耗对国际油价并不敏感.

Resource misallocation is the main cause to structural problems in economic or management fields, and resource allocation optimization is the pivotal solutions. In this paper, we modified hybrid DEA model to be hybrid directional distance function DEA model with radial grouping feature, which adapts to measure resource misallocation level and provides different directions to resource adjustment. In order to verify the validity of this model, a sample with 12 DMUs was designed, resource misallocation levels of those DMUs were measured, and schemes of resource allocation adjustment with different directional distance functions were put forward respectively. Results show that this hybrid DEA model in which imports radial grouping feature can achieve more detailed classification in inefficiency measurement of resource allocation, and the adjustment schemes can be made by choosing directional distance function in light of specific demand of each DMU, therefore personalized customization can be achieved with this model.

"双一流"建设改变了我国高校传统的教育资源配置方式,优化资源配置将是高校取得竞争优势的重要内容.以山东省为例,基于改进的混合方向性距离函数DEA模型,在测度高校教育资源配里效率的基础上分析教育资源错配的原因,按照不同方向性距离函数提出教育资源配置调整的不同方案.研究结果显示,山东省"双一流"建设高校2013～2015年的教育资源配置效率呈现下降趋势,16所高校大部分达到了DEA有效,非DEA有效的6所高校教育资源错配的原因存在较大差异,整体而言产出非效率明显高于投入非效率,科研及设备投入的非效率明显高于人员投入的非效率.引入径向分组的DEA模型可以对教育资源配置非效率进行更细致的划分,资源配置调整方案可以根据高校自身要求选取方向性距离函数,从而实现个性化定制.

Different influences of energy structural adjustment and the technology spillout to industrial ecological level, which is divided into energy consumption intensity and pollutant emission intensity, are studied with panel data of Chinese 22 industry sectors from 2005 to 2014, then evolution features of industrial ecological level are analyzed.Results show that the elasticity coefficients of technology spillout in Chinese industry sectors are-0.148 and-0.198, which are bigger than direct R&D investment significantly.The control effect of energy structural adjustment to emission intensity is significant and elasticity coefficients of coal and oil are bigger than natural gas and non-fossil energy.There exist differences among different sorts of industrial sectors for technology spillout and energy structural adjustment, which are more efficient in lower ecological level sectors.Results of rolling estimation show that the effect of technology spillout gradually enhanced with the promotion of ecological level of individual industrial sector in the last few years, and the elasticity coefficients of coal and oil decreased slowly, which can illuminate the effect of energy structural adjustment.

Aiming to study the driving effect of investment in technology development to control primary energy consumption,IPAT model about primary energy consumption and C-D production function about economic development are established firstly,then parameters of IPAT model and C-D production function are calculated basedon Johansen-Juselius cointegeration test theory.The optimum control model of primary energy consumption is constructed to acquire the optimum path of primary energy consumption and the optimum control path of investment in technology development.The driving function of investment in technology development is analyzed furthermore.With the constraint of economic development during "13th five years",the optimization problem of primary energy consumption is studied,and results show that Chinese primary energy consumption can be controlled to decline continuously in theory in the condition of rapid growth of technology.In 2020,primary energy consumption can decline to 330940 million tons of standard coal,however,Chinese technology development investment should increase sharply with 26.6％ growth rate.With the driving function of technology development,the ratio of total investment in fixed assets to GDP can also decrease continuously,hence the intensive development mode of economy can be achieved with the driving function of technology development.

Based on seven evaluation models,oil and gas investment environments are evaluated individually,the compatibility of different models are tested by fuzzy cluster analysis and Kendall coordination coefficient method to construct the compatible methods set,then an optimal weighted combination evaluation model is established which can eliminate the inconsistencies among the evaluation results and improve the reasonable level.40 countries which dominate oil and gas production market in 5 regions are chosen to be studied,and evolution trends and influence factors ofoil and gas investment environments are also studied with data of 2006-2015.The results show that international oil and gas investment environments incline consistently as a whole from 2006 to 2013,drop dramatically in 2014 and 2015,and vary among different regions.The three biggest influence factors,which show individual characters,include oil and gas potentials,economic environments and basis environments.There are great resource potentials and excellent basis environments in regions of Mideast,Central Asia and Russia,and there are great economic advantages in Asia-Pacific.Political environments aggravate,economic and basis environments are being improved continuously.Resource potentials are getting promoted in Russia and South America.Social environments,law and policy environments have a steady performance.

科技发展投资、固定资产投资、非化石能源生产投资是当前中国经济发展、能源结构调整的重要依托,在当前经济发展和能源结构调整约束下,基于Johansen-Juselius协整检验构建C-D生产函数,在此基础上构建科技发展投资、固定资产投资、非化石能源生产投资的投资决策模型,基于1995-2014年数据及中国《十三五规划》提出的能源结构和经济增长目标,对中国政府的投资决策问题进行研究.结果显示:在“十三五”期间经济发展和能源结构约束下,中国非化石能源产量应至少保持年均7.04％的复合增长率,非化石能源生产投资应至少保持年均9.98％的复合增长率;在保持2014年全社会固定资产投资比重条件下,“十三五”期间中国科技投资应至少保持年均7.94％的复合增长率.提高科技发展投资和提高全社会固定资产投资比重均有助于实现经济发展目标,但提高科技发展投资水平对经济发展的边际效用远高于全社会固定资产投资比重,科技发展投资对全社会固定资产投资有较好的替代效用.

本文基于Shih-Mil s移动学习模型对移动网络环境下改进的SET支付协议模拟实验平台进行了需求分析与功能设计,对模拟实验平台的网络配置环境和业务流程进行了详细设计,最后对模拟实验平台进行了开发实现和教学应用.应用效果显示,情境化的模拟实验教学方式能够适合现阶段高校学生学习方式的转变并较好地提升学生的学习兴趣,实验成效较为明显;智能终端APP能够提供便捷的理论学习和师生交流渠道,学生可以随时随地获取学习资源并进行学习研讨,大大提升了实验效率;模拟实验平台配置简单、成本低廉、通用性强,打破了传统专业信息安全实验室的限制,具有较好的可推广性.

Objective: Taking Dongying of Shandong province as an example, the healthcare reform performance in public hospitals was e-valuated and some suggestions about further reform were put forward.Methods:On the basis of the evaluation indices, Delphi method was taken to calculate the weights of each index.Then the theory of fuzzy comprehensive evaluation in which values of indices were acquired by question-naires was used to evaluate the performance of the new healthcare reform in public hospitals.Results:Probability of good performance in Dongying public hospitals reform reached 35.75%, however the probability of bad performance was still over 50%.Probability of good performance reached 48.66%and 40.48%individually in patient satisfaction level, managing level.Probabilities of no performance were both over 80%in financial state and personnel training.Conclusion:Performance of Dongying public hospitals reform had been embodied on the whole, but there was much room to improve.Some progresses had been made in patience satisfaction level, medical quality and medical efficiency, the financial state and personnel training were still unsatisfactory.Some countermeasures in infrastructure construction, talent introduction, staff cultivation and modern management system perfection should be taken to enhance the reform level successively .

Evaluation index system of oil and gas investment environmentis constructed with literature analysis and expert interview, then the evaluation model is established based on proof theory.Oil and gas investment environments are evaluated comprehensively with the data of 2003-2012 in 10 countries of the Middle Eeast and the evolvement process of individual oil and gas investment environment is analyzed on the results computed by this model, finally the factors giving rise to changes of investment environments are analyzed. Results show that the oil and gas investment environments in the Middle East declined from 2003 to 2014, which are mainly influenced by political situation, economic environment, oil price slump, etc.