道德自律在和谐社会构建中的价值

社会主义和谐社会是民主法治、公平正义、诚信友爱、充满活力、要定有序、人与自然和谐相处的社会。构建社会主义和谐社会需正视道德自律的价值。道德自律在社会主义和谐社会构建中的积极作用,具体表现为:只有大多数人达到道德自律的境界时,社会主义和谐社会的道德风尚与和谐的人际关系才能真正形成;只有大多数人达到道德自律的境界时,对和谐社会的利益关系的调节,才能确保政策、法则落到实处,为社会主义和谐社会的构建创造雄厚的物资基础;只有大多数人达到道德自律的境界时,才能培育并实现和谐精神的现实价值。     

加强大学生民主观教育的思路和对策--大学生民主观教育研究之五

为了使大学生民主观教育取得实效,应当坚持大学生民主观教育的系统性、民主性、科学性等原则;应积极探索大学生民主观教育的途径;应该坚持显性民主观教育与隐性民主观教育相结合,批判资本主义民主观与借鉴其有益成果相结合,民主的理论教育与民主的实践教育相结合;并注重营造良好的民主环境和氛围.     

“冼夫人文化与建设文化大省”学术研讨会述要

由广东省社科联、广东炎黄文化研究会等单位联合主办的“冼夫人文化与建设文化大省”学术研讨会于2005年1月3~6日在广东电白召开,来自广东省内外的专家学者和各界代表共200多人参加了会议,收到论文近80篇,提出许多新观点,新见解,反映了研究有相当的深度。现将有关讨论情况,从学     

大学生学习民主与学术自由观念教育研究

大学生这一特定群体,系统地学习和积极参与科学与技术研究是其在校的主要职责和任务。为此,对大学生进行民主观教育,应该帮助大学生树立学习民主观念,包括学习的主体性观念、学习的教学民主观念、学习的创新观念和学习的自教观念;帮助大学生确立学术自由观念,深刻认识学术自由观念的内涵与价值,坚信学术自由的学术科学精神,承担学术自由的学术责任。     

Eco-friendly container transshipment route scheduling problem with repacking operations

Currently, a huge amount of cargo is transported via containers by liner shipping companies. Under stochastic demand, repacking operations and carbon reduction, which may lead to an increase in effectiveness and environmental improvement, have been rarely considered in previous literature. In this paper, we investigate a container transshipment route scheduling problem with repacking operations under stochastic demand and environmental protection. The problem is a combinatorial optimization problem. Lacking historical data, a chance-constrained programming model is proposed to minimize the total operating and environment-related costs. We choose two distribution-free approaches, i.e., approximation based in Markov’s Inequality and Mixed Integer Second-Order Conic Program to approximate the chance constraints. As the loses induced by unfulfilled demand are not taken into account in the above model, a scenario-based model is developed considering the loses. Risk-neutral model may provide solutions that perform poorly while considering uncertainty. To incorporate decision makers’ perspectives, therefore, we also propose a risk-averse model adopting a risk aversion measure called Conditional Value-at-Risk to meet different preferences. Finally, we conduct computational experiments based on real data to compare the performances of the modeling methods and illustrate the impacts by testing different risk levels and confidence levels.