Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

基于人工免疫的乳品供应链质量安全风险预警研究

针对日益凸显的乳品质量安全问题,建立及时有效的乳品预警机制已经变得至关重要。文中从供应链的角度构建乳品质量安全风险指标体系。结合人工免疫及负选择算法并加以改进,建立基于人工免疫的乳品质量安全风险模型,并对某乳品企业的乳品质量安全风险进行了预警分析。研究结果表明文中建立的乳品质量安全风险预警模型能够定量分析乳品质量安全状态并有效预警,为实施乳品安全供应链监管提供了新思路和方法。     

Neural network detection of management fraud using published financial data

This paper uses a Artificial Neural Network (AutoNet) to develop a model for detecting management fraud. The study offers an in-depth examination of important publicly available predictors of fraudulent financial statements. We find a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements. © 1998 John Wiley & Sons, Ltd.     

基于BP人工神经网络的小城镇生态规划研究——以梅河口市中和镇为例

试图将人工神经网络方法引入生态规划,采用BP(Back-Propagation)神经网络建立生态适宜度评价模型,并用于实际的生态适宜度分析中,从而为生态规划提供一种新的方法。对案例区梅河口市中和镇生态健康水平进行评价,结果显示:中和镇生态现状处于亚健康水平。从生态规划学理论和神经网络模型出发,以梅河口市中和镇实际调查数据为基础,利用MATLAB7.0软件建立生态适宜度分析模型。利用建立的评价模型对中和镇的生态适宜性进行评价。根据生态适宜性评价结果和生态健康水平提出中和镇生态区划方案。     

基于人工神经网络模型的加固专用砂浆性能研究

根据影响加固专用砂浆性能的水胶比、粉煤灰与胶凝材料用量比、包裹砂粒的平均浆体厚度3个因素的正交试验数据,文章建立了人工神经网络模型,并计算了不同因素不同水平下砂浆的稠度、密度、抗折强度、抗压强度和黏结强度,讨论了各个因素和砂浆各个材料性能之间的关系,并为加固专用砂浆的配制及性能指标预测提供了计算方法。     

基于模糊优选法的核电厂址选择外部人为事件的评价方法

出于安全考虑,核电厂在选址阶段要充分考虑厂址周围的外部人为事件。为此,我国出台了《HAF0105核电厂厂址选择的外部人为事件》,对外部人为事件进行了界定,并提出相应的勘查和评价策略。根据《事件》提出的飞机坠毁、爆炸、危险液体释放的分类,进行定量化评价,然后利用模糊优选法建立模型,将多个厂址进行比对,得到待选厂址在外部人为事件方面的优劣排序。     

The Methodological Progress of Tourism Demand Forecasting: A Review of Related Literature

Research on modeling the estimation and forecasting of tourism demand has evolved with increasing sophistication and improved quality. In this study, 155 research papers published between 1995 and 2009 were identified and were classified into three main groups according to the methods and techniques adopted—an econometric-based approach, time series techniques, and artificial intelligence (AI)-based methods. It appears that the more advanced methods such as cointegration, error correction model, time varying parameter model, and their combinations with systems of equations produce better results in terms of forecasting accuracy. We also discuss the implications and suggest future directions of tourism research techniques and methods.     

An agency model to explain trade credit policy and empirical evidence

This article explains trade credit policy based on the agency theory. According to this theory, we have developed an agency model based on the adverse selection and moral hazard phenomena arising from the relation between sellers and buyers. This model has been estimated by using panel data methodology applied to UK companies. Our findings strongly support the model proposed. We find that smaller firms, those with a smaller proportion of fixed assets, and those that are less profitable extend more trade credit, whereas firms with a high proportion of variable costs and high percentage of bad debts extend less.     

论煤炭产业结构调整与重组

会计政策选择是影响企业财务信息质量的一个重要方面。本文对上市公司会计政策变更进行分析 ,并阐明上市公司会计政策变更目的是为了操纵利润     

改进导向下人工智能产业政策比较研究

政府主导推动人工智能产业发展已经成为全球趋势。基于比较公共政策理论,把政策改进作为人工智能产业政策比较研究的目的导向,有助于为政策制定部门进一步完善现行政策文本具体内容提供有益参考。根据政策比较研究一般范式和逻辑,将政策改进导向下的比较分析框架细分为五大模块,对每个模块分别提出基本要求,即政策比较文本先进性、政策比较主体标杆性、政策比较方法科学性、政策比较结果分析因果性、政策比较对策建议适宜性。将人工智能产业政策比较研究现有成果按照基本要求进行系统梳理,研判对每个模块基本要求的满足情况。研究发现,现有成果均不能充分满足各个模块相应的基本要求。鉴于此,针对每个模块提出了下一步改进的主要研究方向。