Constant Angle Property and Canonical Principal Directions for Surfaces in {\mathbb{M}^{2}(c) \times {\mathbb{R}_{1}}}

In this paper, we study surfaces in Lorentzian product spaces ${{\mathbb{M}^{2}(c) \times \mathbb{R}_1}}$ . We classify constant angle spacelike and timelike surfaces in ${{\mathbb{S}^{2} \times \mathbb{R}_1}}$ and ${{\mathbb{H}^{2} \times \mathbb{R}_1}}$ . Moreover, complete classifications of spacelike surfaces in ${{\mathbb{S}^{2} \times \mathbb{R}_1}}$ and ${{\mathbb{H}^{2} \times \mathbb{R}_1}}$ and timelike surfaces in ${{\mathbb{M}^{2}(c) \times \mathbb{R}_1}}$ with a canonical principal direction are obtained. Finally, a new characterization of the catenoid of the 3rd kind is established, as the only minimal timelike surface with a canonical principal direction in Minkowski 3–space.     

具有时滞和反馈控制的离散Leslie系统的概周期解

讨论了具有时滞和反馈控制的离散Leslie概周期捕食与被捕食系统.利用差分不等式和通过构造适当的Lyapunov函数,得到了系统持久性和全局吸引的充分条件.利用泛函概周期的壳理论,得到了系统存在唯一全局吸引概周期解的充分条件.     

二次规划的一种简易算法

本文对二次规划的单纯形算法，从算法到收敛条件均加以改进，得到更简易的程序和收敛准则。     

Heuristic and stochastic methods in optimization

The shifting bottleneck (SB) heuristic is among the most successful approximation methods for solving the job shop problem. It is essentially a machine based decomposition procedure where a series of one machine sequencing problems (OMSPs) are solved. However, such a procedure has been reported to be highly ineffective for the flow shop problems. In particular, we show that for the 2-machine flow shop problem, the SB heuristic will deliver the optimal solution in only a small number of instances. We examine the reason behind the failure of the machine based decomposition method for the flow shop. An optimal machine based decomposition procedure is formulated for the 2-machine flow shop, the time complexity of which is worse than that of the celebrated Johnson’s rule. The contribution of the present study lies in showing that the same machine based decomposition procedures which are so successful in solving complex job shops can also be suitably modified to optimally solve the simpler flow shops.     

A dynamic evaluation framework for ambient air pollution monitoring

Accurate real-time prediction of urban air quality is one of the most important problems in control and improve ambient air condition globally. Therefore, the modeling and applications of air pollutant forecasting and evaluation has attracted the attention of researchers in recent years. Based on the method of fuzzy mathematical synthetic evaluation, this paper built a dynamic evaluation model for the purpose of mastering the future air quality immediately. A newly proposed computational intelligence optimization algorithm is improved to optimize the least square support vector machine, which can generate rolling forecasts of six air pollutants concentration. The information of future air quality status is built by the fuzzy synthetic assessment model based on entropy weighing method. The results and analysis of air quality monitoring show that accurate and reliable forecast of urban air pollutants concentration are possible and the air quality conditions can be evaluated objectively. Through the simulation design, it proves that the proposed dynamic evaluation model can provide a practical tool for ambient air ambient quality evaluation.     

A mathematical model and algorithms for the aircraft hangar maintenance scheduling problem

An aircraft hangar maintenance scheduling problem is studied, motivated by the aircraft heavy maintenance conducted in a hangar operated by an independent maintenance service company. The aircraft hangar maintenance scheduling problem in such context consists of determining a maintenance schedule with minimum penalty costs in fulfilling maintenance requests, and a series of hangar parking plans aligned with the maintenance schedule through the planning period. A mixed-integer linear programming (MILP) mathematical model, integrating the interrelations between the maintenance schedule and aircraft parking layout plans, is presented at first. In the model, the variation of parking capacity of the maintenance hangar and the blocking of the aircraft rolling in and out path are considered. Secondly, the model is enhanced by narrowing down the domain of the time-related decision variables to the possible rolling in and out operations time of each maintenance request. Thirdly, to obtain good quality feasible solutions for large scale instances, a rolling horizon approach incorporating the enhanced mathematical model is presented. The results of computational experiments are reported, showing: (i) the effectiveness of the event-based discrete time MILP model and (ii) the scalability of the rolling horizon approach that is able to provide good feasible solutions for large size instances covering a long planning period.     

农业科技投入与农业经济增长非线性关系研究

对农村科技投入与农业经济增长的实证研究以往多是基于线性模型进行的,而实际受诸多因素影响,二者之间可能存在动态的非线性关系.因此使用我国1980-2010年时间序列数据,运用非线性ESTR对这个问题进行研究,结果表明我国农村科技投入与农业经济增长之间存在单向的格兰杰因果关系,两者存在正向非线性关系,表现出阶段性变化特征.其具体分为三个阶段,即增长期1980-1982年、减速期1982-1986年、平衡期1986-2010年.     

基于非期望产出SBM模型的中国各省份能源效率的实证分析

运用处理非期望产出的SBM模型,对2012年中国28个省份的能源效率进行实证分析,并对各省份及区域的节能减排潜力进行了相应分析.其中,利用因子分析法将废水、废气、固体污染物3种形态的环境污染整理为一个综合指标,视为非期望产出。研究结果表明,非期望产出的引入明显降低了中国区域的平均效率水平,而且中西部地区对环境污染变量的因素比东部地区较为敏感;不同省份的节能减排潜力差异较大,西部地区的节能减排潜力最大,其次是中部,东部的节能减排潜力最低.     

A smoothing heuristic for a bilevel pricing problem

In this paper, we provide a heuristic procedure, that performs well from a global optimality point of view, for an important and difficult class of bilevel programs. The algorithm relies on an interior point approach that can be interpreted as a combination of smoothing and implicit programming techniques. Although the algorithm cannot guarantee global optimality, very good solutions can be obtained through the use of a suitable set of parameters. The algorithm has been tested on large-scale instances of a network pricing problem, an application that fits our modeling framework. Preliminary results show that on hard instances, our approach constitutes an alternative to solvers based on mixed 0–1 programming formulations.     

Improved method of sea level forecasting at Venice (Northern Adriatic Sea)

In this paper a forecasting method for the extremely dangerous aqua alta phenomenon is developed. The city of Venice, which is located in the northeastern part of Italy, is often subjected to intense flooding, due to increasing of the sea level. The classical methods used in Geophysics generally failed in forecasting this phenomenon, because of the complexity of the physical modelling. Therefore, a method based on a non-linear auto-regressive moving average (ARMA) approach is proposed here. It is found that the time series, corresponding to the sea level data, present a Gaussian distribution, but during short periods, of order of few days, a strong non-Gaussian behaviour is evident, which is concomitant with aqua alta events. This suggests the development of a non-linear version of the usual ARMA models. An empirical orthogonal function technique is applied in order to avoid numerical pathologies of the model due to the particular characteristics of the time series. The encouraging results indicate that such technique is a good tool for forecasting and alarm purposes.