新媒体环境下主流媒体舆论引导责任及其路径优化

新媒体环境给舆论表达提供了一个开放空间,承担着舆论引导责任的主流媒体,如何对网络舆论进行有效引导是其当下面临的一项重大课题。新媒体环境下要想发挥好主流媒体的舆论引导功能,需要从四个方面着手:传统主流媒体要充分发挥深度报道、新闻评论的传统优势;新兴主流媒体不仅要占领好、利用好网络这一阵地,而且要培养意见领袖,打造专业微博群;加强传统主流媒体和新兴主流媒体议程设置的互动呼应机制;从现实角度和新媒体技术研发以及网络自身的制衡来优化网络舆论引导的保障体系。     

逆向物流系统激励机制优化研究

随着环境立法日益严格以及市场竞争的加剧,逆向物流日益引起国内外企业管理者和学者的重视。以委托—代理理论为理论基础,通过引入辅助观测变量——制造商对其他零售商回收的废旧产品再处理所得的收益——建立优化模型,可用以分析辅助观测变量和其他因素对激励强度系数和代理成本的影响。分析结果表明:适当引入辅助观测变量能有效降低代理成本,提高激励强度。     

论我国法人治理结构的缺陷与完善

我国在国有大中型企业公司制改革中,由于法人治理结构不完善,致使改制的预定目标难以实现。其公司法人治理结构存在缺陷的深层次原因,是公司股权结构不合理及我国《公司法》自身存在一些缺陷。因此,对国有企业进行现代企业制度改造,应该从优化股权结构和修改公司法的不足入手,努力创建完善的公司法人治理结构。     

产业集群培育与城市功能优化

产业集群对地区经济发展具有积极的作用.集群的形成是多种因素综合作用的结果,本质上依赖于城市功能的支持.城市功能是个系统概念,是要素、结构和支持体系的统一.按照系统性、个性化的原则优化城市功能,可有效培育产业集群,带动地区经济发展.     

反梯度推进:西部产业升级的一种思路

反梯度推进理论的核心内容强调 :落后地区要根据自己的实际情况 ,直接引进世界最先进的技术 ,发展自己的高新技术 ,带动传统产业实现超越发展。近几年 ,我国提出的西部大开发战略 ,其实质就是要解决中国区域经济的均衡协调发展问题 ,实现区域经济的跨越式发展。而实现结构优化和产业升级则是根本出路。在这方面 ,反梯度推进理论为我国西部大开发过程中的产业升级提供了非常重要的启示。     

An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles

The demand for glass bottles is exhibiting an upward trend over time. The manufacturing of glass bottles is costlier in terms of time and resources and is associated with a higher level of heat generation and environmental pollution compared to recycling processes. In response to the aforementioned challenges, companies that use glass bottles need to implement strategies to manage their reverse supply chains in conjunction with their traditional supply chains, as the economic and environmental benefits of returned products are unquestionable. Closed-loop supply chains (CLSCs) integrate forward and reverse flows of products and information. This integration helps companies to have a broader view of the whole chain. Despite these advantages, managing CLSCs can be challenging as they are exposed to many uncertainties regarding supply and demand processes, travel times, and quantity/quality of returned products.In this study, we consider the production planning, inventory management, and vehicle routing decisions of a CLSC of beverage glass bottles. We propose an MILP model and rely on a multi-stage adjustable robust optimization (ARO) formulation to deal with the randomness in both the demand for filled bottles and the requests for pickups of empty bottles. We develop an exact oracle-based algorithm to solve the ARO problem and propose a heuristic search algorithm to reduce the solution time. Our numerical experiments not only show the incompetency of the customary method, namely the affine decision rule approach, but also illustrate how our algorithms can solve the small-size problems and significantly improve the quality of the obtained solution for large problems. Furthermore, our numerical results show that robust plans tend to be sparse, meaning the routes are chosen so that empty bottles are transported to production sites in such a way that fewer new bottles need to be ordered. Thus, robust planning makes the CLSCs more environmentally friendly.     

Reconstruction of populations by stochastic optimization: Sensitivity analysis

The reconstruction of populations by stochastic optimization solves the nontrivial problem of finding demographic flows from population registers or vital statistics and censuses, if available. These flows allow the reconstruction of stocks (age pyramids and vital statistics). After a review of reconstruction methods, the sensitivity analysis shows the robustness of the method by stochastic optimization to flawed or missing values, to the length of the reconstruction period, and to variations in the actual demographic flows.     

A note on the non-negativity of continuous-time ARMA and GARCH processes

A general approach for modeling the volatility process in continuous-time is based on the convolution of a kernel with a non-decreasing Lévy process, which is non-negative if the kernel is non-negative. Within the framework of Continuous-time Auto-Regressive Moving-Average (CARMA) processes, we derive a necessary condition for the kernel to be non-negative, and propose a numerical method for checking the non-negativity of a kernel function. These results can be lifted to solving a similar problem with another approach to modeling volatility via the COntinuous-time Generalized Auto-Regressive Conditional Heteroscedastic (COGARCH) processes.     

混合学习法前向网络多属性储层参数预测

应用前向网络描述地震属性和储层参数间的非线性映射关系时，经典的误差反向传播算法存在收敛速度慢，易陷入局部极值等诸多不足。研究了融合粒子群优化算法和误差反向传播算法的混合学习法前向网络多属性储层参数预测技术。粒子群优化算法是一种群体随机搜索演化计算技术，具有较快的收敛速度和较强的全局搜寻能力；误差反向传播算法本质上是梯度下降算法，注重局部搜索。混合学习法为两种学习算法交替执行，首先以粒子群优化算法训练网络，当误差能量在规定的迭代次数内不再发生变化时，采用误差反向传播算法实现局部寻优。理论函数逼近测试和实际储层参数预测实验说明了混合学习法具有学习时间短、求解效率高、可靠性强的优点，具有良好的应用前景。     

教育媒体与技术在机械制图课程教学中的优化运用

为了获得良好的教学效果,提高教育教学质量,关键是如何实现教育媒体与技术在课程教学中的优化运用。文章在调查研究和理论分析的基础上,建立了基于教育媒体与技术的优化教学模型,重点介绍了教育媒体与技术在机械制图课程教学中优化运用的一些做法和经验。