Assessing the regional digital divide across the European Union-27

Despite the efforts devoted to the analysis of the digital divide over the last years, the regional evidence on this issue is still scarce. Within this context, the goals of this paper are twofold: (1) measuring the digital divide across the regions of the 27 Member States and within each country and (2) explaining the observed regional disparities. The analysis leads to identify Dutch regions as the Top-10 in ICT, while Greece and Bulgaria occupy the Bottom-10. Therefore, results show that the regional digital divide reflects to some extent the income gap. However, regional policy seems to be having some positive implications for technology adoption. In particular, the rural-versus-urban dimension of the digital gap appears to be less important than it is usually claimed to. Likewise, some evidence is found of the role of cultural and institutional factors in ICT adoption, an issue that has not been previously analyzed at the regional level.     

Modeling and optimization of a container drayage problem with resource constraints

This paper investigates the problem faced by firms that transport containers by truck in an environment with resource constraints. The considered area is export-dominant. As a result, there are three types of container movements as inbound full, outbound full, and inbound empty movements. Both the time windows at the terminal and at the customers’ places and the operation times are considered. Empty containers are also regarded as separate transportation resources besides trucks. The total operating time including waiting time of all the trucks in operation is minimized. The problem is first formulated as a directed graph and then mathematically modeled based on the graph. It falls into a multiple traveling salesman problem with time windows (m-TSPTW) with resource constraints. An algorithm based on reactive tabu search (RTS) is developed to solve the problem. A number of randomly generated examples indicate that the algorithm can be applied to the real world.     

The power of dimensional analysis in production systems design

Dimensional Analysis (DA) is a well-known methodology in physics, chemistry and other traditional engineering areas. In its simplest form, DA is used to check the meaningfulness of a set of equations (dimensional homogeneity). In the last century, the dimensional theory has been profoundly investigated: its highest achievement is the Buckingham theorem (or pi-theorem), which states that any equation modelling a physical problem can be rearranged in terms of dimensionless ratios, thus saving variables to be handled, and especially enriching the inner physical knowledge of the studied phenomenon.In this paper we investigate how DA can be applied to Operations Management (OM) topics and which benefits it can bring to researchers in this area. A literature review is performed to clarify the main operative issues regarding DA application (assumptions and limitations); then existing applications of DA to OM are explored, pointing out that few researchers have tried to apply this methodology in the OM research field.Stemming from this analysis, we applied the pi-theorem to the design of a Flexible Manufacturing System. A complex problem, requiring 13 dimensional quantities to be expressed, is first studied via simulation; then DA is applied, reducing the number of variables to 9 dimensionless ratios. The reduced problem has a suitable size to be analytically explored and a regression model is formulated which, compared with the simulation study, offers the same precision in analysing the FMS behaviour, being more compact and powerful. This application shows the potential of DA in OM research, and will hopefully draw the attention of researches to this powerful, but unfamiliar and therefore neglected, methodology.     

Entrepreneurial team's risk-based decision-making: A dynamic game analysis

It is well-known that some cognitive factors such as opportunity perception and risk perception influence entrepreneurial risk-based decision-making (RBDM). This paper proposes some rules of entrepreneur's risk decision-learning to address this RBDM taking cognitive factors into consideration. The learning mechanisms of entrepreneurial team's RBDM is derived based on entrepreneur's perception evolution. The paper employs a system dynamics model to analyze the entrepreneurial team's risk decision-learning. A case study is conducted to demonstrate the entrepreneurial RBDM process with cognitive factors.     

发电设备寿命周期费用影响因素的敏感性分析

阐述了发电设备全寿命周期费用（Life Cycle Cost,LCC）的基本理论及计算方法,针对发电设备LCC的影响因素之间存在的客观联系,假定因素之间相互独立,对其中某2个因素的变动幅度进行了一定的修正,使之更贴近实际。以某2×600MW火电机组为例,选取若干影响因素,求得其全寿命周期费用净现值。对目标净现值进行单因素和多因素敏感性分析,以寻找目标值与影响因素之间所存在的内在联系,为设备投资以及后期优化管理提供决策依据。     

基于主成分分析法的重庆市农民素质评价

农民的素质是制约农村经济发展的关键,为了评价农民的素质,本研究通过对重庆市8个地区的683名农民的调查,用主成分分析方法,从"有文化、懂技术、会经营"三个方面出发,构建了一个评价农民素质的指标体系,并且对重庆市的农民综合素质进行了评价。     

Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models

We extend Diebold and Li’s dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and on the high-yield bonds for the long-term forecast horizon. The dynamic Nelson-Siegel factor state space model, however, becomes appealing on the high-yield bonds in the short-term forecast horizon, where the factor dynamics are more likely time-varying and parameter instability is more probable in the model specification.     

Scoring rules and survey density forecasts

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.     

A measure of regional influence with the analytic network process

The metropolitan region is commonly defined by a socio-spatial network of urban nodes that are linked in territory and function. Such a network is differentiated by size and dominance of the linked nodes, characterized by physical or virtual flows of a wide-ranging variety. The analytic network process (ANP) is a multi-criteria analytic method that measures the influence or dominance of the nodes in a network with feedback. We illustrate how ANP determines county rank as a measure of influence in a metropolitan statistical area (MSA) defined by its interrelated socio-economic and spatial elements qualitatively and quantitatively. We compare the ANP results to similar measures of regional influence in the literature.     

Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company’s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.